21.7.2 Breast Cancer, Mammograms, Analysis, Mammography

Chapter Contents (Back)
Mammograms. Breast Cancer. Medical, Applications. Initially X-Ray analysis.
See also Mammography, Microcalcifications, Detection, Analysis.
See also Mammography, Texture Based Techniques, Wavelets.
See also Radiotherapy, Radiation Therapy, Radiotherapy Planning, X-Ray Images.
See also Elastography Analysis.

Highnam, R.[Ralph], Brady, M.[Michael],
Mammographic Image Analysis,
KluwerFebruary 1999, ISBN 0-7923-5620-9.
WWW Link. BibRef 9902

MiniMammographic Database,
1995
WWW Link. Dataset, Mammography.

DDSM: Digital Database for Screening Mammography,
2000, USF.
HTML Version. Dataset, Mammography.

Bowyer, K.W., Astley, S., (Eds.)
Special Issue: State of the Art in Digital Mammographic Image Analysis,
PRAI(7), No. 6, December 1993, pp. 1309-1503. Full issue. BibRef 9312

Kobatake, H., Yoshinaga, Y.,
Detection of spicules on mammogram based on skeleton analysis,
MedImg(15), No. 3, June 1996, pp. 235-245.
IEEE Top Reference. 0203
BibRef

Rangayyan, R.M., Elfaramawy, N.M., Desautels, J.E.L., Alim, O.A.,
Measures of Acutance and Shape for Classification of Breast-Tumors,
MedImg(16), No. 6, December 1997, pp. 799-810.
IEEE Top Reference. 9803
BibRef

Ma, F.[Fei], Bajger, M.[Mariusz], Slavotinek, J.P.[John P.], Bottema, M.J.[Murk J.],
Two graph theory based methods for identifying the pectoral muscle in mammograms,
PR(40), No. 9, September 2007, pp. 2592-2602.
Elsevier DOI 0705
Adaptive pyramid; Minimum spanning tree; Segmentation; Pectoral muscle; Computer-aided diagnosis BibRef

Ma, F.[Fei], Bajger, M.[Mariusz], Bottema, M.J.[Murk J.],
Automatic Mass Segmentation Based on Adaptive Pyramid and Sublevel Set Analysis,
DICTA09(236-241).
IEEE DOI 0912
BibRef

Bajger, M.[Mariusz], Ma, F.[Fei], Williams, S.[Simon], Bottema, M.J.[Murk J.],
Mammographic Mass Detection with Statistical Region Merging,
DICTA10(27-32).
IEEE DOI 1012
BibRef

Ma, F.[Fei], Yu, L., Bajger, M.[Mariusz], Bottema, M.J.[Murk J.],
Mammogram Mass Classification with Temporal Features and Multiple Kernel Learning,
DICTA15(1-7)
IEEE DOI 1603
Gaussian processes BibRef

Bajger, M.[Mariusz], Ma, F.[Fei], Bottema, M.J.[Murk J.],
Automatic Tuning of MST Segmentation of Mammograms for Registration and Mass Detection Algorithms,
DICTA09(400-407).
IEEE DOI 0912
BibRef

Liu, S.[Sheng], Babbs, C.F., Delp, E.J.,
Multiresolution detection of spiculated lesions in digital mammograms,
IP(10), No. 6, June 2001, pp. 874-884.
IEEE DOI 0106
BibRef
Earlier:
Normal mammogram analysis and recognition,
ICIP98(I: 727-731).
IEEE DOI 9810
BibRef

Liu, S., and Delp, E.J.,
Multiresolution Detection of Stellate Lesions in Mammograms,
ICIP97(II: 109-112).
IEEE DOI BibRef 9700

Kobatake, H., Murakami, M., Takeo, H., Nawano, S.,
Computerized detection of malignant tumors on digital mammograms,
MedImg(18), No. 5, May 1999, pp. 369-378.
IEEE Top Reference. 0110
BibRef

Kobatake, H., Yoshinaga, Y., Murakami, M.,
Automatic detection of malignant tumors on mammogram,
ICIP94(I: 407-410).
IEEE DOI 9411
BibRef

Zhen, L.[Lei], Chan, A.K.,
An artificial intelligent algorithm for tumor detection in screening mammogram,
MedImg(20), No. 7, July 2001, pp. 559-567.
IEEE Top Reference. 0110
BibRef

Sajda, P., Spence, C.D.[Clay Douglas], Pearson, J.,
Learning contextual relationships in mammograms using a hierarchical pyramid neural network,
MedImg(21), No. 3, March 2002, pp. 239-250.
IEEE Top Reference. 0205
BibRef

Spence, C.D.[Clay Douglas], Parra, L.C.[Lucas C.], Sajda, P.,
Detection, Synthesis and Compression in Mammographic Image Analysis with a Hierarchical Image Probability Model,
MMBIA01(xx-yy). 0110
BibRef
Earlier:
Hierarchical Image Probability (HIP) Models,
ICIP00(Vol III: 320-323).
IEEE DOI 0008
BibRef

Scholz, B.,
Towards virtual electrical breast biopsy: Space-frequency music for trans-admittance data,
MedImg(21), No. 6, June 2002, pp. 588-595.
IEEE Top Reference. 0208
BibRef

Kerner, T.E., Paulsen, K.D., Hartov, A., Soho, S.K., Poplack, S.P.,
Electrical impedance spectroscopy of the breast: Clinical imaging results in 26 subjects,
MedImg(21), No. 6, June 2002, pp. 638-645.
IEEE Top Reference. 0208
BibRef

Bagui, S.C.[Subhash C.], Bagui, S.[Sikha], Pal, K.[Kuhu], Pal, N.R.[Nikhil R.],
Breast cancer detection using rank nearest neighbor classification rules,
PR(36), No. 1, January 2003, pp. 25-34.
Elsevier DOI 0210
BibRef

Duchesnay, E.[Edouard], Montois, J.J.[Jean-Jacques], Jacquelet, Y.[Yann],
Cooperative agents society organized as an irregular pyramid: A mammography segmentation application,
PRL(24), No. 14, October 2003, pp. 2435-2445.
Elsevier DOI 0307
BibRef

Richard, F.J.P.[Frédéric J.P.],
A comparative study of markovian and variational image-matching techniques in application to mammograms,
PRL(26), No. 12, September 2005, pp. 1819-1829.
Elsevier DOI 0508
BibRef

Sheshadri, H.S., Kandaswamy, A.,
Detection of Breast Cancer Tumor Based on Morphological Watershed Algorithm,
GVIP(05), No. V5, 2005, pp. 17-21
HTML Version. BibRef 0500

Wirth, M.A., Nikitenko, D., Lyon, J.,
Segmentation of the Breast Region in Mammograms Using a Rule-Based Fuzzy Reasoning Algorithm,
GVIP(05), No. V2, January 2005, pp. 45-54
HTML Version. BibRef 0501

Wirth, M.A.[Michael A.], Nikitenko, D.[Dennis],
Suppression of Stripe Artifacts in Mammograms Using Weighted Median Filtering,
ICIAR05(966-973).
Springer DOI 0509
BibRef

Wirth, M.A., Stapinski, A.,
Segmentation of the breast region in mammograms using snakes,
CRV04(385-392).
IEEE DOI 0408
BibRef

Thangavel, K., Karnan, M., Pethalakshmi, A.,
Performance Analysis of Rough Reduct Algorithms in Mammogram,
GVIP(05), No. V8, 2005, pp. 13-21.
HTML Version. BibRef 0500

Guo, H.[Hong], Nandi, A.K.[Asoke K.],
Breast cancer diagnosis using genetic programming generated feature,
PR(39), No. 5, May 2006, pp. 980-987.
Elsevier DOI 0604
Feature extraction; Genetic programming; Fisher discriminant analysis; Pattern recognition
See also Feature generation using genetic programming with application to fault classification. BibRef

Adiga, U., Malladi, R., Fernandez-Gonzalez, R., Ortiz de Solorzano, C.,
High-Throughput Analysis of Multispectral Images of Breast Cancer Tissue,
IP(15), No. 8, August 2006, pp. 2259-2268.
IEEE DOI 0606
BibRef

Hassanien, A.E.[Aboul Ella],
Fuzzy rough sets hybrid scheme for breast cancer detection,
IVC(25), No. 2, February 2007, pp. 172-183.
Elsevier DOI 0701
Rough sets; Fuzzy image processing; Mammograms; Classification; Feature extraction; Rule and reduct generation; Similarity measure; Gray-level co-occurrence matrices BibRef

Castella, C.[Cyril], Abbey, C.K.[Craig K.], Eckstein, M.P.[Miguel P.], Verdun, F.R.[Francis R.], Kinkel, K.[Karen], Bochud, F.O.[François O.],
Human linear template with mammographic backgrounds estimated with a genetic algorithm,
JOSA-A(24), No. 12, December 2007, pp. B1-B12.
WWW Link. 0801
BibRef

Perconti, P.[Philip], Loew, M.H.[Murray H.],
Salience measure for assessing scale-based features in mammograms,
JOSA-A(24), No. 12, December 2007, pp. B81-B90.
WWW Link. 0801
BibRef

Raundahl, J., Loog, M., Pettersen, P., Tanko, L.B., Nielsen, M.,
Automated Effect-Specific Mammographic Pattern Measures,
MedImg(27), No. 8, August 2008, pp. 1054-1060.
IEEE DOI 0808
BibRef

Egorov, V., Sarvazyan, A.P.,
Mechanical Imaging of the Breast,
MedImg(27), No. 9, September 2008, pp. 1275-1287.
IEEE DOI 0809

See also Prostate Mechanical Imaging: 3-D Image Composition and Feature Calculations. BibRef

Kao, T.J., Boverman, G., Kim, B.S., Isaacson, D., Saulnier, G.J., Newell, J.C., Choi, M.H., Moore, R.H., Kopans, D.B.,
Regional Admittivity Spectra With Tomosynthesis Images for Breast Cancer Detection: Preliminary Patient Study,
MedImg(27), No. 12, December 2008, pp. 1762-1768.
IEEE DOI 0812
BibRef

Verma, B.[Brijesh], McLeod, P.[Peter], Klevansky, A.[Alan],
A novel soft cluster neural network for the classification of suspicious areas in digital mammograms,
PR(42), No. 9, September 2009, pp. 1845-1852.
Elsevier DOI 0905
Pattern classification; Neural networks; Clustering algorithms BibRef

Cao, M., Liang, Y., Shen, C., Miller, K.D., Stantz, K.M.,
Developing DCE-CT to Quantify Intra-Tumor Heterogeneity in Breast Tumors With Differing Angiogenic Phenotype,
MedImg(28), No. 6, June 2009, pp. 861-871.
IEEE DOI 0906
See comment: and Response BibRef

Cao, M., Liang, Y., Stantz, K.M.,
Response to Letter Regarding Article: 'Developing DCE-CT to Quantify Intra-Tumor Heterogeneity in Breast Tumors With Differing Angiogenic Phenotype',
MedImg(29), No. 4, April 2010, pp. 1089-1092.
IEEE DOI 1003

See also Comment on Developing DCE-CT to Quantify Intra-Tumor Heterogeneity in Breast Tumors With Differing Angiogenic Phenotype. BibRef

Abramyuk, A., Wolf, G., Hietschold, V., Haberland, U., van den Hoff, J., Abolmaali, N.,
Comment on 'Developing DCE-CT to Quantify Intra-Tumor Heterogeneity in Breast Tumors With Differing Angiogenic Phenotype',
MedImg(29), No. 4, April 2010, pp. 1088-1089.
IEEE DOI 1003

See also Developing DCE-CT to Quantify Intra-Tumor Heterogeneity in Breast Tumors With Differing Angiogenic Phenotype. BibRef

Masmoudi, H., Hewitt, S.M., Petrick, N., Myers, K.J., Gavrielides, M.A.,
Automated Quantitative Assessment of HER-2/neu Immunohistochemical Expression in Breast Cancer,
MedImg(28), No. 6, June 2009, pp. 916-925.
IEEE DOI 0906
BibRef

Tsui, P.H., Liao, Y.Y., Chang, C.C., Kuo, W.H., Chang, K.J., Yeh, C.K.,
Classification of Benign and Malignant Breast Tumors by 2-D Analysis Based on Contour Description and Scatterer Characterization,
MedImg(29), No. 2, February 2010, pp. 513-522.
IEEE DOI 1002
BibRef

Fang, X.[Xi], Yan, P.K.[Ping-Kun],
Multi-Organ Segmentation Over Partially Labeled Datasets With Multi-Scale Feature Abstraction,
MedImg(39), No. 11, November 2020, pp. 3619-3629.
IEEE DOI 2011
Image segmentation, Feature extraction, Semantics, Training, Biomedical imaging, Annotations, Fuses, Medical image segmentation, multiple datasets BibRef

Yang, M.J.[Mei-Juan], Yuan, Y.[Yuan], Li, X.L.[Xue-Long], Yan, P.K.[Ping-Kun],
Medical Image Segmentation Using Descriptive Image Features,
BMVC11(xx-yy).
HTML Version. 1110
BibRef

Mahr, D.M., Bhargava, R., Insana, M.F.,
Three-Dimensional In Silico Breast Phantoms for Multimodal Image Simulations,
MedImg(31), No. 3, March 2012, pp. 689-697.
IEEE DOI 1203
BibRef

Goenezen, S., Dord, J.F., Sink, Z., Barbone, P.E., Jiang, J., Hall, T.J., Oberai, A.A.,
Linear and Nonlinear Elastic Modulus Imaging: An Application to Breast Cancer Diagnosis,
MedImg(31), No. 8, August 2012, pp. 1628-1637.
IEEE DOI 1208
BibRef

Ashraf, A.B., Gavenonis, S.C., Daye, D., Mies, C., Rosen, M.A., Kontos, D.,
A Multichannel Markov Random Field Framework for Tumor Segmentation With an Application to Classification of Gene Expression-Based Breast Cancer Recurrence Risk,
MedImg(32), No. 4, April 2013, pp. 637-648.
IEEE DOI 1304
BibRef

Akbar, S.[Shazia], McKenna, S.J.[Stephen J.], Amaral, T.[Telmo], Jordan, L.[Lee], Thompson, A.[Alastair],
Spin-context Segmentation of Breast Tissue Microarray Images,
BMVA(2013), No. 1, 2013, pp. 4, 1-11.
PDF File. 1304
BibRef

Sanchez, E.[Eider], Toro, C.[Carlos], Artetxe, A.[Arkaitz], Graña, M.[Manuel], Sanin, C.[Cesar], Szczerbicki, E.[Edward], Carrasco, E.[Eduardo], Guijarro, F.[Frank],
Bridging challenges of clinical decision support systems with a semantic approach. A case study on breast cancer,
PRL(34), No. 14, 2013, pp. 1758-1768.
Elsevier DOI 1308
Clinical decision support system BibRef

Zhang, Y.G.[Yun-Gang], Zhang, B.L.[Bai-Ling], Coenen, F.[Frans], Lu, W.J.[Wen-Jin],
Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles,
MVA(24), No. 7, October 2013, pp. 1405-1420.
WWW Link. 1309
BibRef

Filipczuk, P., Fevens, T., Krzyzak, A., Monczak, R.,
Computer-Aided Breast Cancer Diagnosis Based on the Analysis of Cytological Images of Fine Needle Biopsies,
MedImg(32), No. 12, 2013, pp. 2169-2178.
IEEE DOI 1312
Biomedical imaging BibRef

Jeon, S.[Seokhee],
Haptically Assisting Breast Tumor Detection by Augmenting Abnormal Lump,
IEICE(E97-D), No. 2, February 2013, pp. 361-365.
WWW Link. 1402
BibRef

Han, S.[Seokmin], Kang, D.G.[Dong-Goo],
Tissue Cancellation in Dual Energy Mammography Using a Calibration Phantom Customized for Direct Mapping,
MedImg(33), No. 1, January 2014, pp. 74-84.
IEEE DOI 1402
Poisson distribution BibRef

Kiarashi, N., Lo, J.Y., Lin, Y., Ikejimba, L.C., Ghate, S.V., Nolte, L.W., Dobbins, J.T., Segars, W.P., Samei, E.,
Development and Application of a Suite of 4-D Virtual Breast Phantoms for Optimization and Evaluation of Breast Imaging Systems,
MedImg(33), No. 7, July 2014, pp. 1401-1409.
IEEE DOI 1407
Breast BibRef

Sonntag, D.[Daniel], Weber, M.[Markus], Cavallaro, A.[Alexander], Hammon, M.[Matthias],
Integrating Digital Pens in Breast Imaging for Instant Knowledge Acquisition,
AIMag(35), No. 1, Spring 2014, pp. 26.
DOI Link 1408
Writing notes on the images. BibRef

Krylov, V.A.[Vladimir A.], Nelson, J.D.B.[James D.B.],
Stochastic Extraction of Elongated Curvilinear Structures With Applications,
IP(23), No. 12, December 2014, pp. 5360-5373.
IEEE DOI 1402
Radon transforms BibRef

Krylov, V.A.[Vladimir A.], Taylor, S.[Stuart], Nelson, J.D.B.[James D.B.],
Stochastic Extraction of Elongated Curvilinear Structures in Mammographic Images,
ICIAR13(475-484).
Springer DOI 1307
BibRef

Shahjalal, N.A.[Nashid Alam], Islam, M.J.[Mohammed J.],
Pectoral Muscle Elimination on Mammogram Using K-Means Clustering Approach,
IJCVSP(4), No. 1, 2014, pp. 1.
WWW Link. 1412
BibRef

Casti, P., Mencattini, A., Salmeri, M., Rangayyan, R.M.,
Analysis of Structural Similarity in Mammograms for Detection of Bilateral Asymmetry,
MedImg(34), No. 2, February 2015, pp. 662-671.
IEEE DOI 1502
Accuracy BibRef

Halter, R.J., Hartov, A., Poplack, S.P., diFlorio-Alexander, R., Wells, W.A., Rosenkranz, K.M., Barth, R.J., Kaufman, P.A., Paulsen, K.D.,
Real-Time Electrical Impedance Variations in Women With and Without Breast Cancer,
MedImg(34), No. 1, January 2015, pp. 38-48.
IEEE DOI 1502
bioelectric potentials BibRef

Azghani, M., Kosmas, P., Marvasti, F.,
Microwave Medical Imaging Based on Sparsity and an Iterative Method With Adaptive Thresholding,
MedImg(34), No. 2, February 2015, pp. 357-365.
IEEE DOI 1502
Breast BibRef

Chen, F.Y.[Fei-Yu], Bakic, P.R., Maidment, A.D.A., Jensen, S.T., Shi, X.[Xiquan], Pokrajac, D.D.,
Description and Characterization of a Novel Method for Partial Volume Simulation in Software Breast Phantoms,
MedImg(34), No. 10, October 2015, pp. 2146-2161.
IEEE DOI 1511
Monte Carlo methods BibRef

Barufaldi, B.[Bruno], Abbey, C.K.[Craig K.], Lago, M.A.[Miguel A.], Vent, T.L.[Trevor L.], Acciavatti, R.J.[Raymond J.], Bakic, P.R.[Predrag R.], Maidment, A.D.A.[Andrew D. A.],
Computational Breast Anatomy Simulation Using Multi-Scale Perlin Noise,
MedImg(40), No. 12, December 2021, pp. 3436-3445.
IEEE DOI 2112
Breast, Phantoms, Imaging phantoms, Ligaments, Noise measurement, Computational modeling, Clinical trials, Perlin noise, digital breast tomosynthesis BibRef

Zhong, X., Li, J., Ertl, S.M., Hassemer, C., Fiedler, L.,
A System-Theoretic Approach to Modeling and Analysis of Mammography Testing Process,
SMCS(46), No. 1, January 2016, pp. 126-138.
IEEE DOI 1601
Analytical models BibRef

Ye, F.H.[Fang-Hao], Ji, Z.[Zhong], Ding, W.Z.[Wen-Zheng], Lou, C.G.[Cun-Guang], Yang, S.H.[Si-Hua], Xing, D.[Da],
Ultrashort Microwave-Pumped Real-Time Thermoacoustic Breast Tumor Imaging System,
MedImg(35), No. 3, March 2016, pp. 839-844.
IEEE DOI 1603
Breast BibRef

Wu, L.H.[Ling-Hua], Cheng, Z.W.[Zhong-Wen], Ma, Y.Z.[Yuan-Zheng], Li, Y.J.[Yu-Jing], Ren, M.Y.[Ming-Yang], Xing, D.[Da], Qin, H.[Huan],
A Handheld Microwave Thermoacoustic Imaging System With an Impedance Matching Microwave-Sono Probe for Breast Tumor Screening,
MedImg(41), No. 5, May 2022, pp. 1080-1086.
IEEE DOI 2205
Microwave imaging, Imaging, Acoustics, Microwave antenna arrays, Probes, Microwave antennas, Couplings, Thermoacoustic imaging, handheld BibRef

Porter, E., Bahrami, H., Santorelli, A., Gosselin, B., Rusch, L.A., Popovic, M.,
A Wearable Microwave Antenna Array for Time-Domain Breast Tumor Screening,
MedImg(35), No. 6, June 2016, pp. 1501-1509.
IEEE DOI 1606
Antenna arrays BibRef

Quellec, G., Lamard, M., Cozic, M., Coatrieux, G., Cazuguel, G.,
Multiple-Instance Learning for Anomaly Detection in Digital Mammography,
MedImg(35), No. 7, July 2016, pp. 1604-1614.
IEEE DOI 1608
cancer BibRef

Elmoufidi, A.[Abdelali], El Fahssi, K.[Khalid], Jai-andaloussi, S.[Said], Sekkaki, A.[Abderrahim], Gwenole, Q.[Quellec], Lamard, M.[Mathieu],
Anomaly classification in digital mammography based on multiple-instance learning,
IET-IPR(12), No. 3, March 2018, pp. 320-328.
DOI Link 1802
BibRef

Tan, M., Zheng, B., Leader, J.K., Gur, D.,
Association Between Changes in Mammographic Image Features and Risk for Near-Term Breast Cancer Development,
MedImg(35), No. 7, July 2016, pp. 1719-1728.
IEEE DOI 1608
cancer BibRef

Alaa, A.M., Moon, K.H., Hsu, W., van der Schaar, M.,
ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening,
MultMed(18), No. 10, October 2016, pp. 1942-1955.
IEEE DOI 1610
cancer BibRef

Abreu, P.H.[Pedro Henriques], Santos, M.S.[Miriam Seoane], Abreu, M.H.[Miguel Henriques], Andrade, B.[Bruno], Silva, D.C.[Daniel Castro],
Predicting Breast Cancer Recurrence Using Machine Learning Techniques: A Systematic Review,
Surveys(49), No. 3, December 2016, pp. Article No 52.
DOI Link 1612
Background: Recurrence is an important cornerstone in breast cancer behavior, intrinsically related to mortality. In spite of its relevance, it is rarely recorded in the majority of breast cancer datasets, which makes research in its prediction more difficult. Objectives: To evaluate the performance of machine learning techniques applied to the prediction of breast cancer recurrence. BibRef

Gandomkar, Z., Tay, K., Ryder, W., Brennan, P.C., Mello-Thoms, C.,
iCAP: An Individualized Model Combining Gaze Parameters and Image-Based Features to Predict Radiologists Decisions While Reading Mammograms,
MedImg(36), No. 5, May 2017, pp. 1066-1075.
IEEE DOI 1705
Breast, Cancer, Feature extraction, Gaze tracking, Lesions, Mammography, Solid modeling, Breast, Computer-assisted perception, Mammography BibRef

Wang, J., Ding, H., Bidgoli, F.A., Zhou, B., Iribarren, C., Molloi, S., Baldi, P.,
Detecting Cardiovascular Disease from Mammograms With Deep Learning,
MedImg(36), No. 5, May 2017, pp. 1172-1181.
IEEE DOI 1705
Arteries, Breast, Calcium, Diseases, Machine learning, Mammography, Neural networks, Breast arterial calcification (BAC), coronary artery disease, deep learning, mammography BibRef

Abdel-Nasser, M.[Mohamed], Moreno, A.[Antonio], Rashwan, H.A.[Hatem A.], Puig, D.[Domenec],
Analyzing the evolution of breast tumors through flow fields and strain tensors,
PRL(93), No. 1, 2017, pp. 162-171.
Elsevier DOI 1706
Breast, cancer BibRef

Aghdam, H.H.[Hamed Habibi], Puig, D.[Domenec], Solanas, A.[Agusti],
Adaptive Probabilistic Thresholding Method for Accurate Breast Region Segmentation in Mammograms,
ICPR14(3357-3362)
IEEE DOI 1412
Accuracy BibRef

Pertuz, S.[Said], Julia, C.[Carme], Puig, D.[Domenec],
A Novel Mammography Image Representation Framework with Application to Image Registration,
ICPR14(3292-3297)
IEEE DOI 1412
Breast BibRef

Zheng, Y.S.[Yu-Shan], Jiang, Z.G.[Zhi-Guo], Xie, F.Y.[Feng-Ying], Zhang, H.P.[Hao-Peng], Ma, Y.B.[Yi-Bing], Shi, H.Q.[Hua-Qiang], Zhao, Y.[Yu],
Feature extraction from histopathological images based on nucleus-guided convolutional neural network for breast lesion classification,
PR(71), No. 1, 2017, pp. 14-25.
Elsevier DOI 1707
Feature, extraction BibRef

Nguyen, L., Tosun, A.B., Fine, J.L., Lee, A.V., Taylor, D.L., Chennubhotla, S.C.,
Spatial Statistics for Segmenting Histological Structures in H-E Stained Tissue Images,
MedImg(36), No. 7, July 2017, pp. 1522-1532.
IEEE DOI 1707
Breast tissue, Ducts, Image color analysis, Image segmentation, Sociology, Tumors, Histopathological image analysis, evaluation metrics, graph partitioning, image segmentation, image, statistics BibRef

Pani, S., Saifuddin, S.C., Ferreira, F.I.M., Henthorn, N., Seller, P., Sellin, P.J., Stratmann, P., Veale, M.C., Wilson, M.D., Cernik, R.J.,
High Energy Resolution Hyperspectral X-Ray Imaging for Low-Dose Contrast-Enhanced Digital Mammography,
MedImg(36), No. 9, September 2017, pp. 1784-1795.
IEEE DOI 1709
biological organs, dense breasts, image registration, motion artifacts, Lesions, spectroscopy BibRef

Duraisamy, S.[Saraswathi], Emperumal, S.[Srinivasan],
Computer-aided mammogram diagnosis system using deep learning convolutional fully complex-valued relaxation neural network classifier,
IET-CV(11), No. 8, December 2017, pp. 656-662.
DOI Link 1712
BibRef

Lavoie, B.R., Bourqui, J., Fear, E.C., Okoniewski, M.,
Metrics for Assessing the Similarity of Microwave Breast Imaging Scans of Healthy Volunteers,
MedImg(37), No. 8, August 2018, pp. 1788-1798.
IEEE DOI 1808
Antenna measurements, Radar imaging, Breast, Microwave imaging, Phantoms, Microwaves, radar, medical imaging BibRef

Lajili, R.[Rihab], Kalti, K.[Karim], Touil, A.[Asma], Solaiman, B.[Basel], Ben Amara, N.E.[Najoua Essoukri],
Two-step evidential fusion approach for accurate breast region segmentation in mammograms,
IET-IPR(12), No. 11, November 2018, pp. 1972-1982.
DOI Link 1810
BibRef

Das, A., Nair, M.S., Peter, S.D.,
Sparse Representation Over Learned Dictionaries on the Riemannian Manifold for Automated Grading of Nuclear Pleomorphism in Breast Cancer,
IP(28), No. 3, March 2019, pp. 1248-1260.
IEEE DOI 1812
biological tissues, cancer, cellular biophysics, covariance analysis, Hilbert spaces, image classification, bregman divergences BibRef

O'Loughlin, D., Oliveira, B.L., Santorelli, A., Porter, E., Glavin, M., Jones, E., Popovic, M., O'Halloran, M.,
Sensitivity and Specificity Estimation Using Patient-Specific Microwave Imaging in Diverse Experimental Breast Phantoms,
MedImg(38), No. 1, January 2019, pp. 303-311.
IEEE DOI 1901
Breast, Dielectrics, Image reconstruction, Permittivity, Microwave imaging, Estimation, Microwave, breast, image quality assessment BibRef

Liu, J., Xu, B., Zheng, C., Gong, Y., Garibaldi, J., Soria, D., Green, A., Ellis, I.O., Zou, W., Qiu, G.,
An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA,
MedImg(38), No. 2, February 2019, pp. 617-628.
IEEE DOI 1902
Tumors, Biological tissues, Breast cancer, Solid modeling, Biomarkers, Image segmentation, Image color analysis, H-Score, breast cancer BibRef

Rahman, M.A.[Mohammad Akhlaqur], Jha, R.K.[Rajib Kumar], Gupta, A.K.[Abhishek Kumar],
Gabor phase response based scheme for accurate pectoral muscle boundary detection,
IET-IPR(13), No. 5, 18 April 2019, pp. 771-778.
DOI Link 1904
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Lin, H., Chen, H., Graham, S., Dou, Q., Rajpoot, N., Heng, P.,
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Breast cancer, Image segmentation, Metastasis, Tumors, Task analysis, Image analysis, Histopathology image analysis, metastasis detection BibRef

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Breast cancer, Deep learning, Quality-related, Weakly-supervised, Ranking algorithm BibRef

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Learning Where to See: A Novel Attention Model for Automated Immunohistochemical Scoring,
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IEEE DOI 1911
Predictive models, Task analysis, Tumors, Computational modeling, Pathology, Breast, Training, Deep reinforcement learning, breast cancer BibRef

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Breast, Estrogen, Progesterone, Encoder, Decoder BibRef

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Microwave Imaging of Breast Tumor Using Time-Domain UWB Circular-SAR Technique,
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UWB-SAR, CSAR, microwave imaging, breast tumour BibRef

Wu, N., Phang, J., Park, J., Shen, Y., Huang, Z., Zorin, M., Jastrzebski, S., Févry, T., Katsnelson, J., Kim, E., Wolfson, S., Parikh, U., Gaddam, S., Lin, L.L.Y., Ho, K., Weinstein, J.D., Reig, B., Gao, Y., Toth, H., Pysarenko, K., Lewin, A., Lee, J., Airola, K., Mema, E., Chung, S., Hwang, E., Samreen, N., Kim, S.G., Heacock, L., Moy, L., Cho, K., Geras, K.J.,
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening,
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IEEE DOI 2004
Breast cancer, Task analysis, Biomedical imaging, Predictive models, Training, Deep learning, mammography BibRef

Heidari, M., Mirniaharikandehei, S., Liu, W., Hollingsworth, A.B., Liu, H., Zheng, B.,
Development and Assessment of a New Global Mammographic Image Feature Analysis Scheme to Predict Likelihood of Malignant Cases,
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Lesions, Mammography, Image segmentation, Breast cancer, Feature extraction, Computer-aided diagnosis scheme, global mammographic image feature analysis BibRef

Abdar, M.[Moloud], Zomorodi-Moghadam, M.[Mariam], Zhou, X.[Xujuan], Gururajan, R.[Raj], Tao, X.H.[Xiao-Hui], Barua, P.D.[Prabal D.], Gururajan, R.[Rashmi],
A new nested ensemble technique for automated diagnosis of breast cancer,
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Data mining and machine learning, Breast cancer, Nested ensemble technique, BayesNet classifier, Naïve Bayes classifier BibRef

Sha, Z.J.[Zi-Jun], Hu, L.[Lin], Rouyendegh, B.D.[Babak Daneshvar],
Deep learning and optimization algorithms for automatic breast cancer detection,
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breast cancer, convolutional neural networks, feature extraction, feature selection, image segmentation BibRef

Kumari, V.[Vineeta], Ahmed, A.[Aijaz], Kanumuri, T.[Tirupathiraju], Shakher, C.[Chandra], Sheoran, G.[Gyanendra],
Early detection of cancerous tissues in human breast utilizing near field microwave holography,
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3D phantom, breast cancer, dielectric measurement, holography, tumor detection BibRef

Celik, Y.[Yusuf], Talo, M.[Muhammed], Yildirim, O.[Ozal], Karabatak, M.[Murat], Acharya, U.R.[U Rajendra],
Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images,
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Invasive ductal carcinoma, Whole slide images, Deep learning, Transfer learning BibRef

Shu, X., Zhang, L., Wang, Z., Lv, Q., Yi, Z.,
Deep Neural Networks With Region-Based Pooling Structures for Mammographic Image Classification,
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IEEE DOI 2006
Mammographic image, breast cancer, deep neural networks BibRef

Xu, B., Liu, J., Hou, X., Liu, B., Garibaldi, J., Ellis, I.O., Green, A., Shen, L., Qiu, G.,
Attention by Selection: A Deep Selective Attention Approach to Breast Cancer Classification,
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Histopathological image, reinforcement learning, breast cancer classification, deep learning BibRef

Sharma, S.[Shallu], Mehra, R.[Rajesh],
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Residual learning based CNN for breast cancer histopathological image classification,
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breast cancer, convolutional neural network, data augmentation, deep features, histopathological image, residual learning BibRef

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Pre-trained convolutional neural networks as feature extractors for diagnosis of breast cancer using histopathology,
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breast cancer, computer-aided diagnosis, histopathology, pre-trained convolutional neural network BibRef

Zheng, L.[Lili], Wang, G.X.[Guo-Xiang], Zhang, F.L.[Feng-Lei], Zhao, Q.X.[Qing-Xue], Dai, C.L.[Chun-Lai], Yousefi, N.[Nasser],
Breast cancer diagnosis based on a new improved Elman neural network optimized by meta-heuristics,
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breast cancer, collective animal behavior (CAB) algorithm, computer-aided diagnosis, discrete wavelet transform, Elman neural network BibRef

Ali, M.J.[Muhammad Junaid], Raza, B.[Basit], Shahid, A.R.[Ahmad Raza], Mahmood, F.[Fahad], Yousuf, M.A.[Muhammad Adil], Dar, A.H.[Amir Hanif], Iqbal, U.[Uzair],
Enhancing breast pectoral muscle segmentation performance by using skip connections in fully convolutional network,
IJIST(30), No. 4, 2020, pp. 1108-1118.
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digital mammography, pectoral muscle segmentation BibRef

Salama, W.M.[Wessam M.], Elbagoury, A.M.[Azza M.], Aly, M.H.[Moustafa H.],
Novel breast cancer classification framework based on deep learning,
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Investigation of CdTe, GaAs, Se and Si as Sensor Materials for Mammography,
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IEEE DOI 2012
Detectors, II-VI semiconductor materials, Cadmium compounds, Photonics, Silicon, Phantoms, CdTe, X-ray attenuation efficiency BibRef

Shivhare, E.[Ekta], Saxena, V.N.[Vineeta Nigam],
Breast cancer diagnosis from mammographic images using optimized feature selection and neural network architecture,
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breast cancer diagnosis, hybrid optimization, neural network, optimal feature selection, region growing segmentation BibRef

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Classification of Mammogram Abnormalities Using Legendre Moments,
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Häggmark, I., Shaker, K., Hertz, H.M.,
In Silico Phase-Contrast X-Ray Imaging of Anthropomorphic Voxel-Based Phantoms,
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IEEE DOI 2102
Phantoms, X-ray imaging, Breast, Numerical models, Task analysis, Photonics, In silico imaging, mammography, phase contrast, x-ray BibRef

Jouirou, A.[Amira], Baâzaoui, A.[Abir], Barhoumi, W.[Walid],
Multi-view content-based mammogram retrieval using dynamic similarity and locality sensitive hashing,
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Multi-view information fusion, Multidimensional indexing, Locality sensitive hashing, Dynamic similarity BibRef

Fang, H.[Hong], Fan, H.Y.[Hong-Yu], Lin, S.[Shan], Qing, Z.[Zhang], Sheykhahmad, F.R.[Fatima Rashid],
Automatic breast cancer detection based on optimized neural network using whale optimization algorithm,
IJIST(31), No. 1, 2021, pp. 425-438.
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breast cancer, computer-aided diagnosis, image segmentation, neural networks, whale optimization algorithm BibRef

Hossain, M.M.[Md Murad], Saharkhiz, N.[Niloufar], Konofagou, E.E.[Elisa E.],
Feasibility of Harmonic Motion Imaging Using a Single Transducer: In Vivo Imaging of Breast Cancer in a Mouse Model and Human Subjects,
MedImg(40), No. 5, May 2021, pp. 1390-1404.
IEEE DOI 2105
Harmonic analysis, Imaging, Transducers, Tracking, Mechanical factors, Acoustics, Ultrasonic imaging, high-frequency ARF BibRef

Melekoodappattu, J.G.[Jayesh George], Subbian, P.S.[Perumal Sankar], Queen, M.P.F.[M. P. Flower],
Detection and classification of breast cancer from digital mammograms using hybrid extreme learning machine classifier,
IJIST(31), No. 2, 2021, pp. 909-920.
DOI Link 2105
accuracy, CAD, classification, ELM, FOA, GSO, mammogram, optimization BibRef

Chakravarthy, S.R.S.[S R Sannasi], Rajaguru, H.[Harikumar],
A novel improved crow-search algorithm to classify the severity in digital mammograms,
IJIST(31), No. 2, 2021, pp. 921-954.
DOI Link 2105
breast cancer, classification, crow-search algorithm and chaotic maps, mammogram images, wavelet BibRef

Sharma, S.[Shallu], Mehra, R.[Rajesh], Kumar, S.[Sumit],
Optimised CNN in conjunction with efficient pooling strategy for the multi-classification of breast cancer,
IET-IPR(15), No. 4, 2021, pp. 936-946.
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Sowmyayani, S., Murugan, V.,
Multi-Type Classification Comparison of Mammogram Abnormalities,
IJIG(21), No. 3, July 2021, pp. 2150027.
DOI Link 2107
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Wang, C.[Churan], Li, J.[Jing], Zhang, F.[Fandong], Sun, X.W.[Xin-Wei], Dong, H.[Hao], Yu, Y.Z.[Yi-Zhou], Wang, Y.Z.[Yi-Zhou],
Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification,
IP(30), 2021, pp. 7980-7994.
IEEE DOI 2109
Lesions, Mammography, Data models, Generative adversarial networks, Estimation, Computer science, Breast cancer, Domain Knowledge, Mammogram Classification BibRef

Tardy, M.[Mickael], Mateus, D.[Diana],
Looking for Abnormalities in Mammograms With Self- and Weakly Supervised Reconstruction,
MedImg(40), No. 10, October 2021, pp. 2711-2722.
IEEE DOI 2110
Task analysis, Mammography, Annotations, Training, Image segmentation, Image resolution, Image reconstruction, weakly supervised BibRef

Chakravarthy, S.R.S.[S. R. Sannasi], Rajaguru, H.[Harikumar],
Deep-features with Bayesian optimized classifiers for the breast cancer diagnosis,
IJIST(31), No. 4, 2021, pp. 1861-1881.
DOI Link 2112
breast cancer, deep features, mammogram, optimizable algorithms, transfer learning BibRef

Üncü, Y.A.[Yigit Ali], Sevim, G.[Gençay], Mercan, T.[Tanju], Vural, V.[Veli], Durmaz, E.[Emel], Canpolat, M.[Murat],
Differentiation of tumoral and non-tumoral breast lesions using back reflection diffuse optical tomography: A pilot clinical study,
IJIST(31), No. 4, 2021, pp. 2023-2031.
DOI Link 2112
breast lumps, non-tumoral, reflection diffuse optical tomography, tumoral BibRef

Gupta, I.[Isha], Gupta, S.[Sheifali], Singh, S.[Swati],
Different CNN-based Architectures for Detection of Invasive Ductal Carcinoma in Breast Using Histopathology Images,
IJIG(21), No. 5 2021, pp. 2140003.
DOI Link 2201
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Sevim, G.[Gençay], Üncü, Y.A.[Yigit Ali], Mercan, T.[Tanju], Canpolat, M.[Murat],
Image reconstruction for diffuse optical tomography using bi-conjugate gradient and transpose-free quasi minimal residual algorithms and comparison of them,
IJIST(31), No. 4, 2021, pp. 1894-1905.
DOI Link 2112
bi-conjugate gradient, diffuse optical tomography, image reconstruction, reconstruction techniques, transpose free quasi minimal residual BibRef

Üncü, Y.A.[Yigit Ali], Sevim, G.[Gençay], Canpolat, M.[Murat],
Approaches to Preclinical Studies with Heterogeneous Breast Phantom Using Reconstruction and Three-Dimensional Image Processing Algorithms for Diffuse Optical Imaging,
IJIST(32), No. 1, 2022, pp. 343-353.
DOI Link 2201
3D image processing, bi-cubic interpolation, diffuse optical imaging, Gaussian filtering, transpose-free quasi-minimal residual BibRef

Wang, Y.[Yan], Wang, Z.Z.[Zi-Zhou], Feng, Y.Q.[Yang-Qin], Zhang, L.[Lei],
WDCCNet: Weighted Double-Classifier Constraint Neural Network for Mammographic Image Classification,
MedImg(41), No. 3, March 2022, pp. 559-570.
IEEE DOI 2203
Feature extraction, Breast cancer, Convolutional neural networks, Uncertainty, Neural networks, Deep learning, Lesions, Angular space, softmax loss BibRef

Wimmer, M.[Maria], Sluiter, G.[Gert], Major, D.[David], Lenis, D.[Dimitrios], Berg, A.[Astrid], Neubauer, T.[Theresa], Bühler, K.[Katja],
Multi-Task Fusion for Improving Mammography Screening Data Classification,
MedImg(41), No. 4, April 2022, pp. 937-950.
IEEE DOI 2204
Cancer, Feature extraction, Lesions, Task analysis, Breast, Predictive models, Standards, Mammography, DDSM, CBIS-DDSM, model fusion BibRef

Massimi, L.[Lorenzo], Suaris, T.[Tamara], Hagen, C.K.[Charlotte K.], Endrizzi, M.[Marco], Munro, P.R.T.[Peter R. T.], Havariyoun, G.[Glafkos], Hawker, P.M.S.[P. M. Sam], Smit, B.[Bennie], Astolfo, A.[Alberto], Larkin, O.J.[Oliver J.], Waltham, R.M.[Richard M.], Shah, Z.[Zoheb], Duffy, S.W.[Stephen W.], Nelan, R.L.[Rachel L.], Peel, A.[Anthony], Jones, J.L.[J. Louise], Haig, I.G.[Ian G.], Bate, D.[David], Olivo, A.[Alessandro],
Volumetric High-Resolution X-Ray Phase-Contrast Virtual Histology of Breast Specimens With a Compact Laboratory System,
MedImg(41), No. 5, May 2022, pp. 1188-1195.
IEEE DOI 2205
Histopathology, Spatial resolution, Imaging, Image edge detection, X-ray imaging, Surgery, Computed tomography, X-ray phase contrast BibRef

Kashyap, R.[Ramgopal],
Dilated residual grooming kernel model for breast cancer detection,
PRL(159), 2022, pp. 157-164.
Elsevier DOI 2206
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Rajput, G.[Gunjan], Agrawal, S.[Shashank], Biyani, K.[Kunika], Vishvakarma, S.K.[Santosh Kumar],
Early breast cancer diagnosis using cogent activation function-based deep learning implementation on screened mammograms,
IJIST(32), No. 4, 2022, pp. 1101-1118.
DOI Link 2207
breast cancer classification, convolutional neural network, deep learning, detection, Mias dataset BibRef

Song, J.Q.[Jing-Qi], Zheng, Y.J.[Yuan-Jie], Wang, J.[Jing], Ullah, M.Z.[Muhammad Zakir], Li, X.C.[Xue-Cheng], Zou, Z.X.[Zhen-Xing], Ding, G.[Guocheng],
Multi-feature deep information bottleneck network for breast cancer classification in contrast enhanced spectral mammography,
PR(131), 2022, pp. 108858.
Elsevier DOI 2208
Contrast enhanced spectral mammography, Classification, Deep learning, Multi-feature, Information bottleneck BibRef

Dadsetan, S.[Saba], Arefan, D.[Dooman], Berg, W.A.[Wendie A.], Zuley, M.L.[Margarita L.], Sumkin, J.H.[Jules H.], Wu, S.[Shandong],
Deep learning of longitudinal mammogram examinations for breast cancer risk prediction,
PR(132), 2022, pp. 108919.
Elsevier DOI 2209
Breast cancer, Risk prediction, Deep learning, Digital mammogram, Longitudinal data BibRef

Gargouri, N.[Norhène], Mokni, R.[Raouia], Damak, A.[Alima], Sellami, D.[Dorra], Abid, R.[Riadh],
An automatic breast computer-aided diagnosis scheme based on a weighted fusion of relevant features and a deep CNN classifier,
IET-IPR(16), No. 12, 2022, pp. 3394-3406.
DOI Link 2209
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Saadi, H.[Hayet], Merouani, H.F.[Hayet Farida], Melouah, A.[Ahlem], Guessoum, Z.[Zahia], Lemnadjlia, S.[Saida], Boukabach, N.[Nacereddine],
Multi-agents system for breast tumour detection in mammography by deep learning pre-processing and watershed segmentation,
IJCVR(12), No. 5, 2022, pp. 632-661.
DOI Link 2211
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Sharma, M.[Mukta], Mandloi, A.[Ayush], Bhattacharya, M.[Mahua],
A novel DeepML framework for multi-classification of breast cancer based on transfer learning,
IJIST(32), No. 6, 2022, pp. 1963-1977.
DOI Link 2212
biomedical application, breast cancer cells, deep learning, machine learning, multi-classification BibRef

Ganesan, K.[Kanimozhi], Pichai, S.[Shanmugavadivu], Kavitha, M.S.[Muthu Subash], Takahashi, M.[Masayoshi],
Data imputation in deep neural network to enhance breast cancer detection,
IJIST(32), No. 6, 2022, pp. 2094-2106.
DOI Link 2212
Breast cancer detection, classification, data imputation, encoding, machine learning, multilayer networks BibRef

Yang, X.[Xiao], Xi, X.M.[Xiao-Ming], Wang, K.[Kesong], Sun, L.Y.[Liang-Yun], Meng, L.Z.[Ling-Zhao], Nie, X.S.[Xiu-Shan], Qiao, L.[Lishan], Yin, Y.L.[Yi-Long],
Triple-attention interaction network for breast tumor classification based on multi-modality images,
PR(139), 2023, pp. 109526.
Elsevier DOI 2304
Breast tumor classification, Multi-modality fusion, Triple inter-modality interaction, Intra-modality interaction BibRef

Sujatha, R.[Radhakrishnan], Chatterjee, J.M.[Jyotir Moy], Angelopoulou, A.[Anastassia], Kapetanios, E.[Epaminondas], Srinivasu, P.N.[Parvathaneni Naga], Hemanth, D.J.[Duraisamy Jude],
A transfer learning-based system for grading breast invasive ductal carcinoma,
IET-IPR(17), No. 7, 2023, pp. 1979-1990.
DOI Link 2305
DenseNet121, DenseNet201, InceptionReNetV2, invasive ductal carcinoma (IDC), transfer learning (TL), VGG19 BibRef

Gupta, V.[Vedika], Gaur, H.[Harshit], Vashishtha, S.[Srishti], Das, U.[Uttirna], Singh, V.K.[Vivek Kumar], Hemanth, D.J.[D. Jude],
A fuzzy rule-based system with decision tree for breast cancer detection,
IET-IPR(17), No. 7, 2023, pp. 2083-2096.
DOI Link 2305
convolutional neural nets, decision trees, edge detection, fuzzy control, fuzzy neural nets, fuzzy systems, neural nets BibRef

Kelkar, V.A.[Varun A.], Gotsis, D.S.[Dimitrios S.], Brooks, F.J.[Frank J.], KC, P.[Prabhat], Myers, K.J.[Kyle J.], Zeng, R.[Rongping], Anastasio, M.A.[Mark A.],
Assessing the Ability of Generative Adversarial Networks to Learn Canonical Medical Image Statistics,
MedImg(42), No. 6, June 2023, pp. 1799-1808.
IEEE DOI 2306
Biomedical imaging, Generative adversarial networks, Stochastic processes, Data models, Training, Task analysis, Breast, objective image quality assessment BibRef

Luo, J.M.[Jia-Ming], Tang, Y.Z.[Yong-Zhe], Wang, J.[Jie], Lu, H.T.[Hong-Tao],
USMLP: U-shaped Sparse-MLP network for mass segmentation in mammograms,
IVC(137), 2023, pp. 104761.
Elsevier DOI 2309
Breast mass segmentation, Multi-layer perceptron, U-Net BibRef

Swetha, V., Vadivu, G.,
Classifications of benign and malignant mammogram images using Gabor-modified CNN architecture,
IJIST(33), No. 5, 2023, pp. 1682-1695.
DOI Link 2310
breast cancer, CNN, Gabor, Kirsch's edge detector, mammogram BibRef

Thawkar, S.[Shankar], Katta, V.[Vijay], Parashar, A.R.[Ajay Raj], Singh, L.K.[Law Kumar], Khanna, M.[Munish],
Breast cancer: A hybrid method for feature selection and classification in digital mammography,
IJIST(33), No. 5, 2023, pp. 1696-1712.
DOI Link 2310
adaptive neuro-fuzzy inference system, artificial neural network, breast cancer, classification, mammography BibRef

Kiliçarslan, G.[Gülhan], Koç, C.[Canan], Özyurt, F.[Fatih], Gül, Y.[Yeliz],
Breast lesion classification using features fusion and selection of ensemble ResNet method,
IJIST(33), No. 5, 2023, pp. 1779-1795.
DOI Link 2310
breast lesion, classification, computer-aided diagnosis, fused ResNet, SVM, ultrasound image BibRef

Grover, P.[Priyanka], Singh, H.S.[Hari Shankar], Sahu, S.K.[Sanjay Kumar],
Design and analysis of a super compact UWB antenna for accurate detection of breast tumors using monostatic radar-based microwave imaging technique,
IJIST(33), No. 6, 2023, pp. 2100-2117.
DOI Link 2311
breast tumor, microwave imaging, monostatic, specific absorption rate, UWB antenna BibRef

Zhang, J.[Jie], Zhang, Z.C.[Zhi-Chao], Liu, H.[Hua], Xu, S.Q.[Shi-Qiang],
SaTransformer: Semantic-aware transformer for breast cancer classification and segmentation,
IET-IPR(17), No. 13, 2023, pp. 3789-3800.
DOI Link 2311
biomedical imaging, cancer, convolutional neural nets, diseases, image classification, image segmentation BibRef

Li, Y.H.[Yong-Hao], Shen, Y.Q.[Yi-Qing], Zhang, J.D.[Jia-Dong], Song, S.J.[Shu-Jie], Li, Z.H.[Zhen-Hui], Ke, J.[Jing], Shen, D.G.[Ding-Gang],
A Hierarchical Graph V-Net With Semi-Supervised Pre-Training for Histological Image Based Breast Cancer Classification,
MedImg(42), No. 12, December 2023, pp. 3907-3918.
IEEE DOI Code:
WWW Link. 2312
BibRef

Guo, W.[Wei], Li, X.M.[Xiao-Min], Gong, Z.X.[Zhao-Xuan], Zhang, G.D.[Guo-Dong], Jiang, X.[Xiran],
Multiloss strategy for breast cancer subtype classification using digital breast tomosynthesis,
IJIST(34), No. 1, 2024, pp. e22978.
DOI Link 2401
breast cancer subtype classification, decomposed attention block, digital breast tomosynthesis, multiloss strategy BibRef

Rautela, K.[Kamakshi], Kumar, D.[Dinesh], Kumar, V.[Vijay],
Improved GAN for image resolution enhancement using ViT for breast cancer detection,
IJIST(34), No. 2, 2024, pp. e22998.
DOI Link 2402
breast cancer, digital mammography, feature extraction, GAN, transformer BibRef

Munshi, R.M.[Raafat M.], Cascone, L.[Lucia], Alturki, N.[Nazik], Saidani, O.[Oumaima], Alshardan, A.[Amal], Umer, M.[Muhammad],
A novel approach for breast cancer detection using optimized ensemble learning framework and XAI,
IVC(142), 2024, pp. 104910.
Elsevier DOI 2402
Breast cancer detection, Image processing, Healthcare, Transfer learning, Ensemble learning, Deep convoluted features BibRef

Shukla, P.K.[Praveen Kumar], Behera, A.R.[Aditya Ranjan],
A framework for breast cancer prediction and classification using deep learning,
IJCVR(14), No. 2, 2024, pp. 154-169.
DOI Link 2403
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Liang, Y.[Yinhao], Tang, W.J.[Wen-Jie], Wang, T.[Ting], Ng, W.W.Y.[Wing W. Y.], Chen, S.[Siyi], Jiang, K.[Kuiming], Wei, X.H.[Xin-Hua], Jiang, X.[Xinqing], Guo, Y.[Yuan],
HRadNet: A Hierarchical Radiomics-Based Network for Multicenter Breast Cancer Molecular Subtypes Prediction,
MedImg(43), No. 3, March 2024, pp. 1225-1236.
IEEE DOI 2403
Breast cancer, Biomedical imaging, Radiomics, Metadata, Training, Medical diagnostic imaging, Magnetic resonance imaging, multilayer features BibRef

Yi, S.[Sanli], Chen, Z.Y.[Zi-Yan], She, F.[Furong], Wang, T.W.[Tian-Wei], Yang, X.[Xuelian], Chen, D.[Dong], Luo, X.M.[Xiao-Mao],
IDC-Net: Breast cancer classification network based on BI-RADS 4,
PR(150), 2024, pp. 110323.
Elsevier DOI 2403
Breast imaging reporting and data system(BI-RADS), Subcategories 4a-4c, Breast ultrasound images, CNN, CapsNet, IDC-Net BibRef

Chakraborty, D.[Debapriya], Palit, S.[Sarbani], Bhattacharya, U.[Ujjwal],
Deep Classification of Mammographic Breast Density: DCBARNet,
IVCNZ23(1-6)
IEEE DOI 2403
Learning systems, Imaging, Breast, Network architecture, Prediction algorithms, Classification algorithms, attention mechanism BibRef

Patel, V.[Vivek], Chaurasia, V.[Vijayshri],
Efficient breast cancer diagnosis using multi-level progressive feature aggregation based deep transfer learning system,
IJIST(34), No. 3, 2024, pp. e23050.
DOI Link 2404
breast cancer classification, computer aided diagnosis, deep transfer learning, feature aggregation, feature fusion, spatial domain learning BibRef

Lopez, E.[Eleonora], Betello, F.[Filippo], Carmignani, F.[Federico], Grassucci, E.[Eleonora], Comminiello, D.[Danilo],
Attention-map augmentation for hypercomplex breast cancer classification,
PRL(182), 2024, pp. 140-146.
Elsevier DOI Code:
WWW Link. 2405
Attention mechanism, Attention maps, Hypercomplex neural networks, Breast cancer screening, Histopathological images BibRef

Carrasco, K.[Karen], Tomala, L.[Lenin], Meza, E.R.[Eileen Ramirez], Bolanos, D.M.[Doris Meza], Montalvan, W.R.[Washington Ramirez],
Computational Techniques in PET/CT Image Processing for Breast Cancer: A Systematic Mapping Review,
Surveys(56), No. 8, April 2024, pp. 197.
DOI Link 2405
Survey, Breast Cancer. PET/CT, breast cancer, preprocessing, segmentation, feature extraction, classification, datasets BibRef

Berghouse, M.[Marc], Bebis, G.[George], Tavakkoli, A.[Alireza],
Exploring the influence of attention for whole-image mammogram classification,
IVC(147), 2024, pp. 105062.
Elsevier DOI 2406
Mammogram classification, Attention deep, Learning BibRef

Kumar, M.[Mohan], Khatri, S.I.K.[Sun-Il Kumar], Mohammadian, M.[Masoud],
Collation of performance parameters on various machine learning algorithms for breast cancer discernment,
IJCVR(14), No. 4, 2024, pp. 355-374.
DOI Link 2407
BibRef

Yu, X.H.[Xiao-Hui], Tian, J.[Jingjun], Chen, Z.P.[Zhi-Peng], Meng, Y.Z.[Yi-Zhen], Zhang, J.[Jun],
Predictive breast cancer diagnosis using ensemble fuzzy model,
IVC(148), 2024, pp. 105146.
Elsevier DOI 2407
Breast cancer diagnosis, Ensemble, Deep learning, Fuzzy logic, Inception V3, Medical imaging BibRef

Wang, K.[Kang], Zheng, F.[Feiyang], Cheng, L.[Lan], Dai, H.N.[Hong-Ning], Dou, Q.[Qi], Qin, J.[Jing],
Breast Cancer Classification From Digital Pathology Images via Connectivity-Aware Graph Transformer,
MedImg(43), No. 8, August 2024, pp. 2854-2865.
IEEE DOI Code:
WWW Link. 2408
BibRef

Liu, Y.[Yang], Zhu, Y.Q.[Yi-Qi], Gu, Z.[Zhehao], Pan, J.S.[Jin-Shan], Li, J.C.[Jun-Cheng], Fan, M.[Ming], Li, L.H.[Li-Hua], Zeng, T.Y.[Tie-Yong],
Enhanced dual contrast representation learning with cell separation and merging for breast cancer diagnosis,
CVIU(247), 2024, pp. 104065.
Elsevier DOI 2408
Cell separation and merging, Contrast representation learning, Breast cancer diagnosis, Deep learning, Image classification, Sam BibRef

Han, B.[Bowen], Sun, L.[Luhao], Li, C.[Chao], Yu, Z.Y.[Zhi-Yong], Jiang, W.Z.[Wen-Zong], Liu, W.F.[Wei-Feng], Tao, D.P.[Da-Peng], Liu, B.[Baodi],
Deep Location Soft-Embedding-Based Network With Regional Scoring for Mammogram Classification,
MedImg(43), No. 9, September 2024, pp. 3137-3148.
IEEE DOI 2409
Feature extraction, Lesions, Mammography, Solid modeling, Image segmentation, Convolutional neural networks, Training, feature reweighting BibRef


Mohanta, A.[Anindita], Roy, S.D.[Sourav Dey], Nath, N.[Niharika], Bhowmik, M.K.[Mrinal Kanti],
Novel Meta Attention Guided Framework for Breast Abnormality Classification with Combination of FSL and DA,
ICIP24(2930-2936)
IEEE DOI 2411
Adaptation models, Accuracy, Databases, Predictive models, Breast cancer, Few shot learning, Few-shot learning, classification BibRef

Jiang, S.[Siyao], Wu, H.[Huisi], Chen, J.Y.[Jun-Yang], Zhang, Q.[Qin], Qin, J.[Jing],
PH-Net: Semi-Supervised Breast Lesion Segmentation via Patch-Wise Hardness,
CVPR24(11418-11427)
IEEE DOI Code:
WWW Link. 2410
Training, Image segmentation, Adaptation models, Ultrasonic imaging, Shape, Perturbation methods, Contrastive learning BibRef

Cardoso, M.[Miguel], Santiago, C.[Carlos], Nascimento, J.C.[Jacinto C.],
Using Counterfactual Information for Breast Classification Diagnosis,
DEF-AI-MIA24(4996-5002)
IEEE DOI 2410
Training, Visualization, Accuracy, Refining, Machine learning, Radiology, Entropy BibRef

Hasan, Y.[Yumnah], Khan, T.[Talhat], de Bulnes, D.R.F.[Darian Reyes Fernández], Albarracín, J.F.H.[Juan F. H.], Ryan, C.[Conor],
A Comparative Analysis of Implicit Augmentation Techniques for Breast Cancer Diagnosis Using Multiple Views,
EnhanceMedIm24(2345-2354)
IEEE DOI 2410
Wavelet transforms, Training, Image analysis, Training data, Feature extraction, Data augmentation, Delta-sigma modulation, 1D CNN BibRef

Araújo, D.J., Verdelho, M.R., Bissoto, A., Nascimento, J.C., Santiago, C., Barata, C.,
Key Patches Are All You Need: A Multiple Instance Learning Framework For Robust Medical Diagnosis,
DEF-AI-MIA24(5231-5240)
IEEE DOI Code:
WWW Link. 2410
Image analysis, Pipelines, MIMICs, Focusing, Breast cancer, Mirrors BibRef

Nguyen, T.H.[Thanh-Huy], Kha, Q.H.[Quang Hien], Truong, T.N.T.[Thai Ngoc Toan], Lam, B.T.[Ba Thinh], Ngo, B.H.[Ba Hung], Dinh, Q.V.[Quang Vinh], Le, N.Q.K.[Nguyen Quoc Khanh],
Towards Robust Natural-Looking Mammography Lesion Synthesis on Ipsilateral Dual-Views Breast Cancer Analysis,
CVAMD23(2556-2565)
IEEE DOI 2401
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Moroz-Dubenco, C.[Cristiana], Diosan, L.[Laura], Andreica, A.[Anca],
Towards an Unsupervised Growcut Algorithm for Mammography Segmentation,
CVS23(102-111).
Springer DOI 2312
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Zhang, K.[Kunkun], Wang, B.[Bin],
Classification Task Assisted Segmentation Network for Breast Tumor Segmentation in Ultrasound Images,
ICIP23(3294-3298)
IEEE DOI 2312
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Oh, Y.T.[Young-Tack], Ko, E.[Eunsook], Park, H.[Hyunjin],
Semi-supervised Breast Lesion Segmentation Using Local Cross Triplet Loss for Ultrafast Dynamic Contrast-enhanced Mri,
ACCV22(VI:203-217).
Springer DOI 2307
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Godishala, A.K.[Aruna Kranthi], Yassin, H.[Hayati], Veena, R., Lai, D.T.C.[Daphne Teck Ching],
Breast Cancer Tumor Image Classification Using Deep Learning Image Data Generator,
ICIVC22(418-423)
IEEE DOI 2301
Deep learning, Support vector machines, Costs, Uncertainty, Detectors, Breast cancer, Generators, deep learning techniques, image detector BibRef

Wang, L.C.[Li-Chun], Hai, Z.[Zerui], Lu, Y.[Ya], Wang, K.[Kunkun], Wang, Q.[Qian], Zhou, X.L.[Xiao-Ling], Zhang, Z.X.[Zhao-Xia],
Microwave Breast Imaging Based on Deep Learning,
ICIVC22(749-755)
IEEE DOI 2301
Image coding, Neural networks, Phantoms, Breast, Microwave theory and techniques, Real-time systems, electromagnetic inverse scattering BibRef

Lou, J.X.[Jian-Xun], Lin, H.[Hanhe], Marshall, D.[David], White, R.[Richard], Yang, Y.[Young], Shelmerdine, S.[Susan], Liu, H.T.[Han-Tao],
Predicting Radiologist Attention During Mammogram Reading with Deep and Shallow High-Resolution Encoding,
ICIP22(961-965)
IEEE DOI 2211
Training, Performance evaluation, Visualization, Image coding, Graphical models, Image representation, Predictive models, deep learning BibRef

Gong, R.L.[Rong-Lin], Ying, S.H.[Shi-Hui], Shi, J.[Jun],
Task-Driven Self-Supervised BI-Channel Networks Learning for Diagnosis of Breast Cancers with Mammography,
ICIP22(551-555)
IEEE DOI 2211
Knowledge engineering, Image analysis, Design automation, Gray-scale, Breast cancer, Mammography, Classification algorithms, Mammography BibRef

Mandache, D., Guillaume, E.B.à.L.[E. Benoit à La], Badachi, Y., Olivo-Marin, J.C., Meas-Yedid, V.,
The Lifecycle of a Neural Network in the Wild: A Multiple Instance Learning Study on Cancer Detection from Breast Biopsies Imaged with Novel Technique,
ICIP22(3601-3605)
IEEE DOI 2211
Training, Solid modeling, Adaptation models, Biomedical optical imaging, Annotations, cancer BibRef

Abdelli, A.[Adel], Saouli, R.[Rachida], Djemal, K.[Khalifa], Youkana, I.[Imane],
Combined Datasets For Breast Cancer Grading Based On Multi-CNN Architectures,
IPTA20(1-7)
IEEE DOI 2206
Solid modeling, Neural networks, Tools, Breast cancer, Convolutional neural networks, Task analysis, histological magnification factors BibRef

Silva, W.[Wilson], Carvalho, M.[Maria], Mavioso, C.[Carlos], Cardoso, M.J.[Maria J.], Cardoso, J.S.[Jaime S.],
Deep Aesthetic Assessment and Retrieval of Breast Cancer Treatment Outcomes,
IbPRIA22(108-118).
Springer DOI 2205
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Oliveira, H.P.[Helder P.], Cardoso, J.S.[Jaime S.], Magalhaes, A.[Andre], Cardoso, M.J.[Maria J.],
Simultaneous detection of prominent points on breast cancer conservative treatment images,
ICIP12(2841-2844).
IEEE DOI 1302
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Derbel, N.[Nouha], Tmar, H.[Hedi], Mahfoudhi, A.[Adel],
A Multi-View DCNN Based Method for Breast Cancer Screening,
DICTA21(1-6)
IEEE DOI 2201
Limiting, Databases, Digital images, Transfer learning, Neural networks, Predictive models, Delta-sigma modulation, DDSM BibRef

Villareal, R.J.T.[Rosiel Jazmine T.], Abu, P.A.R.[Patricia Angela R.],
Patch-Based Convolutional Neural Networks for TCGA-BRCA Breast Cancer Classification,
ISVC21(II:29-40).
Springer DOI 2112
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Rakhlin, A., Tiulpin, A., Shvets, A.A., Kalinin, A.A., Iglovikov, V.I., Nikolenko, S.,
Breast Tumor Cellularity Assessment Using Deep Neural Networks,
VRMI19(371-380)
IEEE DOI 2004
Image segmentation, Feature extraction, Decoding, Tumors, Breast cancer, Estimation, Deep Neural Networks, Cellularity, Diagnostic BibRef

Cao, Z., Yang, Z., Zhuo, X., Lin, R., Wu, S., Huang, L., Han, M., Zhang, Y., Ma, J.,
DeepLIMa: Deep Learning Based Lesion Identification in Mammograms,
VRMI19(362-370)
IEEE DOI 2004
Mammography, Breast, Lesions, Machine learning, Task analysis, Neural networks, Mammography, Lesion detection, Deep learning BibRef

Lee, H., Kim, S.T., Ro, Y.M.,
Building a Breast-Sentence Dataset: Its Usefulness for Computer-Aided Diagnosis,
VRMI19(440-449)
IEEE DOI 2004
cancer, learning (artificial intelligence), mammography, medical image processing, natural language processing, Visual pointing BibRef

Saini, M.[Manisha], Susan, S.[Seba],
Data Augmentation of Minority Class with Transfer Learning for Classification of Imbalanced Breast Cancer Dataset Using Inception-V3,
IbPRIA19(I:409-420).
Springer DOI 1910
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Oliveira, H.S.[Hugo S.], Teixeira, J.F.[João F.], Oliveira, H.P.[Hélder P.],
Lightweight Deep Learning Pipeline for Detection, Segmentation and Classification of Breast Cancer Anomalies,
CIAP19(II:707-715).
Springer DOI 1909
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Liu, X.F.[Xiao-Feng], Zou, Y.[Yang], Song, Y.H.[Yu-Hang], Yang, C.[Chao], You, J.[Jane], Kumar, B.V.K.V.[B. V. K. Vijaya],
Ordinal Regression with Neuron Stick-Breaking for Medical Diagnosis,
BioIm18(VI:335-344).
Springer DOI 1905
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Domingues, I., Abreu, P.H., Santos, J.,
Bi-Rads Classification of Breast Cancer: A New Pre-Processing Pipeline for Deep Models Training,
ICIP18(1378-1382)
IEEE DOI 1809
Training, Machine learning, Databases, Breast cancer, Mammography, Image pre-processing, Deep learning, BI-RADS classification BibRef

Weiss, N.[Nick], Kost, H.[Henning], Homeyer, A.[André],
Towards Interactive Breast Tumor Classification Using Transfer Learning,
ICIAR18(727-736).
Springer DOI 1807
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Liu, K.C.[Ke-Chun], Mokhtari, M.[Mojgan], Li, B.B.[Bei-Bin], Nofallah, S.[Shima], May, C.[Caitlin], Chang, O.[Oliver], Knezevich, S.[Stevan], Elmore, J.[Joann], Shapiro, L.G.[Linda G.],
Learning Melanocytic Proliferation Segmentation in Histopathology Images from Imperfect Annotations,
CVMI21(3761-3770)
IEEE DOI 2109
Image segmentation, Visualization, Annotations, Biopsy, Pipelines, Melanoma BibRef

Mehta, S., Mercan, E., Bartlett, J., Weaver, D., Elmore, J., Shapiro, L.G.,
Learning to Segment Breast Biopsy Whole Slide Images,
WACV18(663-672)
IEEE DOI 1806
biological tissues, decoding, feature extraction, image classification, image resolution, image segmentation, Semantics BibRef

Lin, H., Chen, H., Dou, Q., Wang, L., Qin, J., Heng, P.A.,
ScanNet: A Fast and Dense Scanning Framework for Metastastic Breast Cancer Detection from Whole-Slide Image,
WACV18(539-546)
IEEE DOI 1806
cancer, computerised tomography, feature extraction, gynaecology, image classification, medical image processing, tumours, Training BibRef

Nahid, A.A., Kong, Y.,
Local and Global Feature Utilization for Breast Image Classification by Convolutional Neural Network,
DICTA17(1-6)
IEEE DOI 1804
biological organs, convolution, feature extraction, image classification, medical image processing, neural nets, Kernel BibRef

Dong, Y., Shen, X.J., Wang, L.J., Wornyo, D., Zha, Z.J.,
Diversity-induced weighted classifier ensemble learning,
ICIP17(1232-1236)
IEEE DOI 1803
Breast cancer, Diversity reception, Heart, Minimization, Sonar, Stability criteria, Training, classifier diversity, weighted classifier BibRef

Yi, C.Q.[Cong-Qin], Zhou, R.Y.[Ru-Yan], Hu, K.N.[Ke-Ning],
Fuzzy Support Vector Machine for breast cancer gene classification,
ICIVC17(676-679)
IEEE DOI 1708
Programming, Support vector machines, FSVM, SVM, classification, gene BibRef

Shrivastava, A., Chaudhary, A., Kulshreshtha, D., Singh, V.P.[V. Prakash], Srivastava, R.,
Automated digital mammogram segmentation using Dispersed Region Growing and Sliding Window Algorithm,
ICIVC17(366-370)
IEEE DOI 1708
Classification algorithms, Image analysis, Image segmentation, Labeling, Mammography, CAD, Dispersed Region Growing Algorithm (DRGA), Sliding Window Algorithm (SWA), image segmentation, mammography BibRef

Bayramoglu, N., Kannala, J., Heikkilä, J.,
Deep learning for magnification independent breast cancer histopathology image classification,
ICPR16(2440-2445)
IEEE DOI 1705
Breast cancer, Databases, Microscopy, Pathology, Training, Training, data BibRef

Alcântara, R.[Rafaela], Junior, P.F.[Perfilino Ferreira], Ramos, A.[Aline],
Tsallis Entropy Extraction for Mammographic Region Classification,
CIARP16(451-458).
Springer DOI 1703
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Dhahbi, S.[Sami], Barhoumi, W.[Walid], Zagrouba, E.[Ezzeddine],
Content-Based Mammogram Retrieval Using Mixed Kernel PCA and Curvelet Transform,
ACIVS16(582-590).
Springer DOI 1611
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Král, P., Lenc, L.,
LBP features for breast cancer detection,
ICIP16(2643-2647)
IEEE DOI 1610
Breast cancer BibRef

Verma, R., Kumar, N., Sethi, A., Gann, P.H.,
Detecting multiple sub-types of breast cancer in a single patient,
ICIP16(2648-2652)
IEEE DOI 1610
Breast cancer BibRef

Fiallos, C.B., Pérez, M.G., Conci, A., Andaluz, V.H.,
Automatic detection of injuries in mammograms using image analysis techniques,
WSSIP15(245-248)
IEEE DOI 1603
cancer BibRef

Khan, N.[Nabeel], Wang, K.[Kaier], Chan, A.[Ariane], Highnam, R.[Ralph],
Automatic BI-RADS Classification of Mammograms,
PSIVT15(475-487).
Springer DOI 1602
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Rodriguez, J.C.[Juan Cruz], González, G.[Germán], Fresno, C.[Cristobal], Fernández, E.A.[Elmer A.],
Integrative Functional Analysis Improves Information Retrieval in Breast Cancer,
CIARP15(43-50).
Springer DOI 1511
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Galdran, A.[Adrian], Picón, A.[Artzai], Garrote, E.[Estibaliz], Pardo, D.[David],
Pectoral Muscle Segmentation in Mammograms Based on Cartoon-Texture Decomposition,
IbPRIA15(587-594).
Springer DOI 1506
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Oliver, A.[Arnau], Llado, X.[Xavier], Torrent, A.[Albert], Marti, J.[Joan],
One-shot segmentation of breast, pectoral muscle, and background in digitised mammograms,
ICIP14(912-916)
IEEE DOI 1502
Breast BibRef

Gharsalli, L., Duchene, B., Mohammad-Djafari, A., Ayasso, H.,
A gradient-like variational Bayesian approach: Application to microwave imaging for breast tumor detection,
ICIP14(1708-1712)
IEEE DOI 1502
Approximation methods BibRef

Ayasso, H., Duchene, B., Mohammad-Djafari, A.,
A variational Bayesian approach for frequency diverse non-linear microwave imaging,
ICIP12(2069-2072).
IEEE DOI 1302
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Nguyen, P.[Phuoc], Tran, D.[Dat], Huang, X.[Xu], Ma, W.L.[Wan-Li],
A Novel Sphere-Based Maximum Margin Classification Method,
ICPR14(620-624)
IEEE DOI 1412
Breast cancer BibRef

Moftah, H., Ibrahim, M., Hassanien, A.E., Schaefer, G.,
Mammary Gland Tumor Detection in Cats Using Ant Colony Optimisation,
ACPR13(942-945)
IEEE DOI 1408
ant colony optimisation BibRef

Deshpande, D.S., Rajurkar, A.M., Manthalkar, R.M.,
Medical image analysis an attempt for mammogram classification using texture based association rule mining,
NCVPRIPG13(1-5)
IEEE DOI 1408
cancer BibRef

Mustra, M.[Mario], Peros, G., Zovko-Cihlar, B.,
Comparison of segmentation accuracy for different LUTs applied to digital mammograms,
WSSIP15(113-116)
IEEE DOI 1603
biological tissues BibRef

Mustra, M.[Mario], Grgic, M.[Mislav], Delac, K.,
Efficient presentation of DICOM mammography images using Matlab,
WSSIP08(13-16).
IEEE DOI 0806
Code, Mammography. BibRef

Les, T.[Tomasz], Markiewicz, T.[Tomasz], Osowski, S.[Stanislaw], Cichowicz, M.[Marzena], Kozlowski, W.[Wojciech],
Automatic Evaluation System of FISH Images in Breast Cancer,
ICISP14(332-339).
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Chen, Z.L.[Zhi-Li], Wang, L.P.[Li-Ping], Denton, E.[Erika],
A Multiscale Blob Representation of Mammographic Parenchymal Patterns and Mammographic Risk Assessment,
CAIP13(II:346-353).
Springer DOI 1311
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Mourainst, D.C.[Daniel Cardoso], Lópezinst, M.A.G.[Miguel Angel Guevara], Cunhainst, P.[Pedro],
Benchmarking Datasets for Breast Cancer Computer-Aided Diagnosis (CADx),
CIARP13(I:326-333).
Springer DOI 1311
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He, W.[Wenda], Zwiggelaar, R.[Reyer],
Breast Parenchymal Pattern Analysis in Digital Mammography: Associations between Tabár and Birads Tissue Compositions,
CAIP13(II:386-393).
Springer DOI 1311
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Arias, J.A.[José Anibal], Rodríguez, V.[Verónica], Miranda, R.[Rosebet],
Meaningful Features for Computerized Detection of Breast Cancer,
CIARP13(II:198-205).
Springer DOI 1311
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Selwyna, P.G.C.[P. Georgia Chris], Loganathan, P.R.[Priyadarshini Ravandhu], Begam, K.H.[K. Haseena],
Development of electrochemical biosensor for breast cancer detection using gold nanoparticle doped CA 15-3 antibody and antigen interaction,
ICSIPR13(75-81).
IEEE DOI 1304
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Kim, D.H.[Dae Hoe], Choi, J.Y.[Jae Young], Ro, Y.M.[Yong Man],
Region based stellate features for classification of mammographic spiculated lesions in computer-aided detection,
ICIP12(2821-2824).
IEEE DOI 1302
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Kumar, M.S.[M. Sathish], Dinesh, E., Mohan Raj, T.,
Involuntary diagnosis of intraductal breast images using gaussian mixture model,
IMVIP12(113-116).
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Krawczyk, B.[Bartosz], Jelen, l.[lukasz], Krzyzak, A.[Adam], Fevens, T.[Thomas],
Oversampling Methods for Classification of Imbalanced Breast Cancer Malignancy Data,
ICCVG12(483-490).
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Detection of breast tumor candidates using marker-controlled watershed segmentation and morphological analysis,
Southwest12(1-4).
IEEE DOI 1205
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Sardar, S.[Santu], Mishra, A.K.[Amit K.],
An improved algorithm For UWB based imaging of breast tumors,
ICIIP11(1-6).
IEEE DOI 1112
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Abdaheer, M.S., Khan, E.[Ekram],
An automatic and simple breast tumor classification using area matching,
ICIIP11(1-5).
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Chaudhury, A.R.[Amrita Ray], Iyer, R.[Ranjani], Iychettira, K.K.[Kaveri K.], Sreedevi, A.,
Diagnosis of Invasive Ductal Carcinoma using image processing techniques,
ICIIP11(1-6).
IEEE DOI 1112
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Vani, G., Savitha, R., Sundararajan, N.,
Classification of abnormalities in digitized mammograms using Extreme Learning Machine,
ICARCV10(2114-2117).
IEEE DOI 1109
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ICIP10(4421-4424).
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ICPR10(2374-2377).
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IEEE DOI 0410
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Khademi, A.[April], Sahba, F.[Farhang], Venetsanopoulos, A.[Anastasios], Krishnan, S.[Sridhar],
Region, Lesion and Border-Based Multiresolution Analysis of Mammogram Lesions,
ICIAR09(802-813).
Springer DOI 0907
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Philip, R.C.[Rohit C.], Rodriguez, J.J.[Jeffrey J.], Gillies, R.J.[Robert J.],
Seed pruning using a multi-resolution approach for automated segmentation of breast cancer tissue,
ICIP08(1436-1439).
IEEE DOI 0810
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Jin, Y.W.[Yuan-Wei], Jiang, Y.[Yi], Moura, J.M.F.[Jose M.F.],
Time Reversal Beamforming for Microwave Breast Cancer Detection,
ICIP07(V: 13-16).
IEEE DOI 0709
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Sánchez-Ferrero, G.V.[Gonzalo V.], Arribas, J.I.[Juan Ignacio],
A Statistical-Genetic Algorithm to Select the Most Significant Features in Mammograms,
CAIP07(189-196).
Springer DOI 0708
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Foggia, P.[Pasquale], Percannella, G.[Gennaro], Sansone, C.[Carlo], Vento, M.[Mario],
A Graph-Based Clustering Method and Its Applications,
BVAI07(277-287).
Springer DOI 0710
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Foggia, P., Guerriero, M., Percannella, G., Sansone, C., Tufano, F., Vento, M.,
A Graph-Based Method for Detecting and Classifying Clusters in Mammographic Images,
SSPR06(484-493).
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Ribeiro da Silva, V.[Valdeci], Cardoso de Paiva, A.[Anselmo], Corrêa Silva, A.[Aristófanes], Muniz de Oliveira, A.C.[Alexandre Cesar],
Semivariogram Applied for Classification of Benign and Malignant Tissues in Mammography,
ICIAR06(II: 570-579).
Springer DOI 0610
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Roller, D.[Dieter], Lampasona, C.[Constanza],
A Method for Interpreting Pixel Grey Levels in Digital Mammography,
ICIAR06(II: 580-588).
Springer DOI 0610
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Ketsetzis, G.[Georgios], Brady, M.[Michael],
Optimizing the selection of Flip Angle acquisitions for T1 measurement in Breast,
MMBIA06(97).
IEEE DOI 0609
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Lee, S.[Sarah], Stathaki, T.[Tania],
Mammogram Analysis Using Two-Dimensional Autoregressive Models: Sufficient or Not?,
CIAP05(900-906).
Springer DOI 0509
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Vitulano, S.[Sergio], Casanova, A.[Andrea],
The Role of Entropy: Mammogram Analysis,
ICIAR08(xx-yy).
Springer DOI 0806
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Bornefalk, H.[Hans],
Use of Quadrature Filters for Detection of Stellate Lesions in Mammograms,
SCIA05(649-658).
Springer DOI 0506
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Mohammed, S., Yang, L.[Lei], Fiaidhi, J.,
A dynamic fuzzy classifier for detecting abnormalities in mammograms,
CRV04(172-179).
IEEE DOI 0408
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Christoyianni, I., Dermatas, E., Kokkinakis, G.,
Automatic Detection of Abnormal Tissue in Mammography,
ICIP01(II: 877-880).
IEEE DOI 0108
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McGarry, G., Deriche, M.,
Mammographic Image Segmentation Using a Tissue-mixture Model and Markov Random Fields,
ICIP00(Vol III: 416-419).
IEEE DOI 0008
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Banerjee, A., Chellappa, R.,
Tumor Detection in Digital Mammograms,
ICIP00(Vol III: 432-435).
IEEE DOI 0008
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Sari-Sarraf, H., and Gleason, S.S.,
A Novel Approach to Computer-Aided Diagnosis of Mammographic Images,
WACV96(230-235).
IEEE DOI 9609
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Morrison, S.[Steven], Linnett, L.M.[Laurie M.],
A Model Based Approach to Object Detection in Digital Mammography,
ICIP99(II:182-186).
IEEE DOI BibRef 9900

Jiang, H.[Hao], Tiu, W.[Wilson], Yamamoto, S.[Shinji], Iisaku, S.I.[Shun-Ichi],
Detection of Spicules in Mammograms,
ICIP97(III: 520-523).
IEEE DOI BibRef 9700
And:
Automatic recognition of spicules in mammograms,
CIAP97(II: 396-403).
Springer DOI 9709
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Marroquin, E.M., Vos, C., Santamaria, E., Jove, X., Socoro, J.C.,
Nonlinear image analysis for fuzzy classification of breast cancer,
ICIP96(II: 943-946).
IEEE DOI 9610
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Cheng, H.D., Chen, C.H., Freimanis, R.I.,
A neural network for breast cancer detection using fuzzy entropy approach,
ICIP95(III: 141-144).
IEEE DOI 9510
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Hutt, J.W., Astley, S.M., Boggis, C.R.M.,
Computer Aided Detection of Abnormalities in Mammograms,
BMVC94(xx-yy).
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Dengler, J., Guckes, M.,
Estimating a global shape model for objects with badly defined boundaries,
ICPR92(II:381-384).
IEEE DOI 9208
mammography application BibRef

Biwas, S.[Soma], Zhao, F.[Fei], Li, X.X.[Xiao-Xing], Mullick, R.[Rakesh], Vaidya, V.[Vivek],
Lesion Detection in Breast Ultrasound Images Using Tissue Transition Analysis,
ICPR14(1185-1188)
IEEE DOI 1412
Breast BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Breast Cancer Cell Analysis, Pathology, Nuclei Detection .


Last update:Nov 26, 2024 at 16:40:19