te Brake, G.M.,
Karssemeijer, N.,
Single and multiscale detection of masses in digital mammograms,
MedImg(18), No. 7, July 1999, pp. 628-639.
IEEE Top Reference.
0110
BibRef
Karssemeijer, N.,
te Brake, G.M.,
Detection of stellate distortions in mammograms,
MedImg(15), No. 5, October 1996, pp. 611-619.
IEEE Top Reference.
0203
BibRef
Hupse, R.,
Karssemeijer, N.,
Use of Normal Tissue Context in Computer-Aided Detection of Masses in
Mammograms,
MedImg(28), No. 12, December 2009, pp. 2033-2041.
IEEE DOI
0912
BibRef
Timp, S.,
Varela, C.,
Karssemeijer, N.,
Temporal Change Analysis for Characterization of Mass Lesions in
Mammography,
MedImg(26), No. 7, July 2007, pp. 945-953.
IEEE DOI
0707
BibRef
Polakowski, W.E.,
Cournoyer, D.A.,
Rogers, S.K.,
Desimio, M.P.,
Ruck, D.W.,
Hoffmeister, J.W.,
Raines, R.A.,
Computer-Aided Breast-Cancer Detection and Diagnosis of Masses Using
Difference of Gaussians and Derivative-Based Feature Saliency,
MedImg(16), No. 6, December 1997, pp. 811-819.
IEEE Top Reference.
9803
BibRef
Constantinidis, A.S.,
Fairhurst, M.C.,
Rahman, A.F.R.,
A new multi-expert decision combination algorithm and its application
to the detection of circumscribed masses in digital mammograms,
PR(34), No. 8, August 2001, pp. 1527-1537.
Elsevier DOI
0105
BibRef
Sahiner, B.,
Petrick, N.,
Chan, H.P.[Heang-Ping],
Hadjiiski, L.M.,
Paramagul, C.,
Helvie, M.A.,
Gurcan, M.N.,
Computer-aided characterization of mammographic masses:
Accuracy of mass segmentation and its effects on characterization,
MedImg(20), No. 12, December 2001, pp. 1275-1284.
IEEE Top Reference.
0201
BibRef
Hadjiiski, L.M.,
Sahiner, B.,
Chan, H.P.[Heang-Ping],
Petrick, N.,
Helvie, M.A.,
Classification of malignant and benign masses based on hybrid ART2LDA
approach,
MedImg(18), No. 12, December 1999, pp. 1178-1187.
IEEE Top Reference.
0110
BibRef
Hatanaka, Y.,
Hara, T.,
Fujita, H.,
Kasai, S.,
Endo, T.[Tokiko],
Iwase, T.,
Development of an automated method for detecting mammographic masses
with a partial loss of region,
MedImg(20), No. 12, December 2001, pp. 1209-1214.
IEEE Top Reference.
0201
BibRef
Lo, S.C.B.[Shih-Chung B.],
Li, H.[Huai],
Wang, Y.[Yue],
Kinnard, L.,
Freedman, M.T.,
A multiple circular path convolution neural network system for
detection of mammographic masses,
MedImg(21), No. 2, February 2002, pp. 150-158.
IEEE Top Reference.
0204
BibRef
Li, H.,
Liu, K.,
Lo, S.C., and
Wang, Y.,
Stochastic Model and Probabilistic Decision-Based Classifier for
Mass Detection in Digital Mammography,
ICIP97(III: 539-542).
IEEE DOI
BibRef
9700
Eltonsy, N.H.,
Tourassi, G.D.,
Elmaghraby, A.S.,
A Concentric Morphology Model for the Detection of Masses in
Mammography,
MedImg(26), No. 6, June 2007, pp. 880-889.
IEEE DOI
0706
BibRef
Castella, C.[Cyril],
Eckstein, M.P.[Miguel P.],
Abbey, C.K.[Craig K.],
Kinkel, K.[Karen],
Verdun, F.R.[Francis R.],
Saunders, R.S.,
Samei, E.,
Bochud, F.O.[François O.],
Mass detection on mammograms:
Influence of signal shape uncertainty on human and model observers,
JOSA-A(26), No. 2, February 2009, pp. 425-436.
WWW Link.
0902
BibRef
Dominguez, A.R.[Alfonso Rojas],
Nandi, A.K.[Asoke K.],
Toward breast cancer diagnosis based on automated segmentation of
masses in mammograms,
PR(42), No. 6, June 2009, pp. 1138-1148.
Elsevier DOI
0902
Breast cancer; Breast masses; Mammography; Image analysis
BibRef
Cao, A.[Aize],
Song, Q.[Qing],
Yang, X.L.[Xu-Lei],
Robust information clustering incorporating spatial information for
breast mass detection in digitized mammograms,
CVIU(109), No. 1, January 2008, pp. 86-96.
Elsevier DOI
0801
Robust information clustering; Minimax optimization of mutual information;
Spatial information
BibRef
Cao, A.[Aize],
Song, Q.[Qing],
Yang, X.L.[Xu-Lei],
Wang, L.[Lei],
Breast mass segmentation based on information theory,
ICPR04(III: 758-761).
IEEE DOI
0409
BibRef
de Oliveira Martins, L.[Leonardo],
Junior, G.B.[Geraldo Braz],
Corrêa Silva, A.[Aristófanes],
Cardoso de Paiva, A.[Anselmo],
Gattass, M.[Marcelo],
Detection of Masses in Digital Mammograms using K-Means and Support
Vector Machine,
ELCVIA(8), No. 2, July 2009, pp. xx-yy.
DOI Link
1002
BibRef
Neto, O.P.S.,
Carvalho, O.,
Sampaio, W.,
Corrêa Silva, A.[Aristófanes],
Cardoso de Paiva, A.[Anselmo],
Automatic segmentation of masses in digital mammograms using particle
swarm optimization and graph clustering,
WSSIP15(109-112)
IEEE DOI
1603
evolutionary computation
BibRef
Muralidhar, G.S.,
Bovik, A.C.,
Giese, J.D.,
Sampat, M.P.,
Whitman, G.J.,
Haygood, T.M.,
Stephens, T.W.,
Markey, M.K.,
Snakules: A Model-Based Active Contour Algorithm for the Annotation of
Spicules on Mammography,
MedImg(29), No. 10, October 2010, pp. 1768-1780.
IEEE DOI
1011
BibRef
Muralidhar, G.S.[Gautam S.],
Markey, M.K.[Mia K.],
Bovik, A.C.[Alan C.],
Snakules for automatic classification of candidate spiculated mass
locations on mammography,
Southwest10(197-200).
IEEE DOI
1005
BibRef
Earlier: A1, A3, A2:
Snakules: Snakes that seek spicules on mammography,
ICIP10(4373-4376).
IEEE DOI
1009
BibRef
Sampat, M.P.,
Wang, Z.[Zhou],
Markey, M.K.,
Whitman, G.J.,
Stephens, T.W.,
Bovik, A.C.,
Measuring Intra- and Inter-Observer Agreement in Identifying and
Localizing Structures in Medical Images,
ICIP06(81-84).
IEEE DOI
0610
BibRef
Jahanbin, R.[Rana],
Sampat, M.P.[Mehul P.],
Muralidhar, G.S.[Gautam S.],
Whitman, G.J.[Gary J.],
Bovik, A.C.[Alan C.],
Markey, M.K.[Mia K.],
Automated Region of Interest Detection of Spiculated Masses on Digital
Mammograms,
Southwest08(129-132).
IEEE DOI
0803
BibRef
Muralidhar, G.S.[Gautam S.],
Bovik, A.C.[Alan C.],
Markey, M.K.[Mia K.],
A Steerable, Multiscale Singularity Index,
SPLetters(20), No. 1, January 2013, pp. 7-10.
IEEE DOI
1212
BibRef
And:
A new singularity index,
ICIP12(1873-1876).
IEEE DOI
1302
BibRef
Sampat, M.P.,
Markey, M.K.,
Bovik, A.C.,
Measurement and Detection of Spiculated Lesions,
Southwest06(105-109).
IEEE DOI
0603
BibRef
Wang, Y.[Ying],
Tao, D.C.[Da-Cheng],
Gao, X.B.[Xin-Bo],
Li, X.L.[Xue-Long],
Wang, B.[Bin],
Mammographic mass segmentation: Embedding multiple features in
vector-valued level set in ambiguous regions,
PR(44), No. 9, September 2011, pp. 1903-1915.
Elsevier DOI
1106
Mass segmentation; Computer-aided diagnose; Vector-valued level set;
Relaxed shape constraint; Mammograms
See also Relay Level Set Method for Automatic Image Segmentation, A.
BibRef
Palma, G.[Giovanni],
Bloch, I.[Isabelle],
Muller, S.[Serge],
Detection of masses and architectural distortions in digital breast
tomosynthesis images using fuzzy and a contrario approaches,
PR(47), No. 7, 2014, pp. 2467-2480.
Elsevier DOI
1404
Digital breast tomosynthesis
BibRef
Tsochatzidis, L.[Lazaros],
Zagoris, K.[Konstantinos],
Arikidis, N.[Nikolaos],
Karahaliou, A.[Anna],
Costaridou, L.[Lena],
Pratikakis, I.E.[Ioannis E.],
Computer-aided diagnosis of mammographic masses based on a supervised
content-based image retrieval approach,
PR(71), No. 1, 2017, pp. 106-117.
Elsevier DOI
1707
Mammography
BibRef
Liu, J.,
Zhang, S.T.[Shao-Ting],
Liu, W.,
Deng, C.,
Zheng, Y.,
Metaxas, D.N.,
Scalable Mammogram Retrieval Using Composite Anchor Graph Hashing
With Iterative Quantization,
CirSysVideo(27), No. 11, November 2017, pp. 2450-2460.
IEEE DOI
1712
Binary codes, Breast cancer, Databases, Mammography,
Quantization (signal), Visualization, Composite hashing,
scalable
BibRef
Jiang, M.[Menglin],
Zhang, S.T.[Shao-Ting],
Metaxas, D.N.[Dimitris N.],
Detection of Mammographic Masses by Content-Based Image Retrieval,
MLMI14(33-41).
Springer DOI
1410
BibRef
Sajeev, S.[Shelda],
Bajger, M.[Mariusz],
Lee, G.[Gobert],
Superpixel texture analysis for classification of breast masses in
dense background,
IET-CV(12), No. 6, September 2018, pp. 779-786.
DOI Link
1808
BibRef
Gu, S.H.[Sheng-Hua],
Chen, Y.[Yi],
Sheng, F.Q.[Fang-Qing],
Zhan, T.M.[Tian-Ming],
Chen, Y.J.[Yun-Jie],
A novel method for breast mass segmentation: from superpixel to
subpixel segmentation,
MVA(30), No. 7-8, October 2019, pp. 1111-1122.
Springer DOI
1911
BibRef
Gnanasekaran, V.S.[Vaira Suganthi],
Joypaul, S.[Sutha],
Sundaram, P.M.[Parvathy Meenakshi],
Chairman, D.D.[Durga Devi],
Deep learning algorithm for breast masses classification in mammograms,
IET-IPR(14), No. 12, October 2020, pp. 2860-2868.
DOI Link
2010
BibRef
Xu, S.Z.[Sheng-Zhou],
Adeli, E.[Ehsan],
Cheng, J.Z.[Jie-Zhi],
Xiang, L.[Lei],
Li, Y.[Yang],
Lee, S.W.[Seong-Whan],
Shen, D.G.[Ding-Gang],
Mammographic mass segmentation using multichannel and multiscale
fully convolutional networks,
IJIST(30), No. 4, 2020, pp. 1095-1107.
DOI Link
2011
fully convolutional network, mammogram, mass segmentation,
multichannel, multiscale
BibRef
Shen, T.,
Gou, C.,
Wang, J.,
Wang, F.,
Simultaneous Segmentation and Classification of Mass Region From
Mammograms Using a Mixed-Supervision Guided Deep Model,
SPLetters(27), 2020, pp. 196-200.
IEEE DOI
2002
Mixed-supervision, deep learning,
segmentation and classification, mammogram
BibRef
Andreadis, T.[Theofilos],
Emmanouilidis, C.[Christodoulos],
Goumas, S.[Stefanos],
Koulouriotis, D.[Dimitrios],
Development of an intelligent CAD system for mass detection in
mammographic images,
IET-IPR(14), No. 10, August 2020, pp. 1960-1966.
DOI Link
2008
BibRef
Cao, X.,
Chen, H.,
Li, Y.,
Peng, Y.,
Wang, S.,
Cheng, L.,
Uncertainty Aware Temporal-Ensembling Model for Semi-Supervised ABUS
Mass Segmentation,
MedImg(40), No. 1, January 2021, pp. 431-443.
IEEE DOI
2012
Uncertainty, Image segmentation, Breast, Reliability, Training,
Biomedical imaging,
breast mass
BibRef
Chandraraju, T.S.[Thirumarai Selvi],
Jeyaprakash, A.[Amudha],
Categorization of breast masses based on deep belief network
parameters optimized using chaotic krill herd optimization algorithm
for frequent diagnosis of breast abnormalities,
IJIST(32), No. 5, 2022, pp. 1561-1576.
DOI Link
2209
altered phase preserving dynamic range compression (APPDRC),
breast cancer, chaotic krill herd optimization (CKHO),
deep belief network (DBN)
BibRef
Sun, L.[Lilei],
Wen, J.[Jie],
Wang, J.Q.[Jun-Qian],
Zhang, Z.[Zheng],
Zhao, Y.[Yong],
Zhang, G.Y.[Gui-Ying],
Xu, Y.[Yong],
Breast mass classification based on supervised contrastive learning
and multi-view consistency penalty on mammography,
IET-Bio(11), No. 6, 2022, pp. 588-600.
DOI Link
2212
BibRef
Pan, A.[Ansi],
Xu, S.Z.[Sheng-Zhou],
Mammographic mass recognition using feature reuse and channel
attention mechanism,
IJIST(32), No. 6, 2022, pp. 2154-2162.
DOI Link
2212
breast cancer, convolutional neural network, mass recognition, mammogram
BibRef
Chakravarthy, S.R.S.[S. R. Sannasi],
Rajaguru, H.[Harikumar],
SKMAT-U-Net architecture for breast mass segmentation,
IJIST(32), No. 6, 2022, pp. 1880-1888.
DOI Link
2212
breast cancer, convolution neural network, loss function,
segmentation, ultrasound
BibRef
Liu, Y.H.[Yu-Hang],
Zhang, F.D.[Fan-Dong],
Chen, C.Q.[Chao-Qi],
Wang, S.W.[Si-Wen],
Wang, Y.Z.[Yi-Zhou],
Yu, Y.Z.[Yi-Zhou],
Act Like a Radiologist: Towards Reliable Multi-View Correspondence
Reasoning for Mammogram Mass Detection,
PAMI(44), No. 10, October 2022, pp. 5947-5961.
IEEE DOI
2209
Cognition, Mammography, Visualization, Solid modeling,
Bipartite graph, Semantics, Proposals, Detection, mammogram
BibRef
Zhang, J.[Jiadong],
Cui, Z.M.[Zhi-Ming],
Zhou, L.P.[Lu-Ping],
Sun, Y.Q.[Yi-Qun],
Li, Z.[Zhenhui],
Liu, Z.[Zaiyi],
Shen, D.G.[Ding-Gang],
Breast Fibroglandular Tissue Segmentation for Automated BPE
Quantification With Iterative Cycle-Consistent Semi-Supervised
Learning,
MedImg(42), No. 12, December 2023, pp. 3944-3955.
IEEE DOI Code:
WWW Link.
2312
BibRef
Li, G.B.[Guo-Bin],
Zwiggelaar, R.[Reyer],
Feature learning based on connectivity estimation for unbiased
mammography mass classification,
CVIU(238), 2024, pp. 103884.
Elsevier DOI
2312
Breast cancer, Texture features, Deep learned features, Interpretability
BibRef
Ma, J.[Jiechao],
Li, X.[Xiang],
Li, H.W.[Hong-Wei],
Wang, R.X.[Rui-Xuan],
Menze, B.[Bjoern],
Zheng, W.S.[Wei-Shi],
Cross-View Relation Networks for Mammogram Mass Detection,
ICPR21(8632-8638)
IEEE DOI
2105
Pathology, Analytical models, Image analysis, Performance gain,
Feature extraction, Mammography, Pattern recognition, Cross-view,
Mammogram
BibRef
Valdés-Santiago, D.[Damian],
Quintana-Martínez, R.[Raúl],
León-Mecías, Á.[Ángela],
Díaz-Romañach, M.L.B.[Marta Lourdes Baguer],
Mammographic Mass Segmentation Using Fuzzy C-means and Decision Trees,
AMDO18(1-10).
Springer DOI
1807
BibRef
Cardoso, J.S.,
Marques, N.,
Dhungel, N.,
Carneiro, G.,
Bradley, A.P.,
Mass segmentation in mammograms: A cross-sensor comparison of deep
and tailored features,
ICIP17(1737-1741)
IEEE DOI
1803
Databases, Machine learning, Mammography, Manuals, Shape,
Task analysis, Training, Mammogram, cross-sensor, mass segmentation, \
transfer learning
BibRef
Goubalan, S.R.T.J.,
Goussard, Y.,
Maaref, H.,
Unsupervised malignant mammographic breast mass segmentation
algorithm based on pickard Markov random field,
ICIP16(2653-2657)
IEEE DOI
1610
Breast
BibRef
Rodríguez-López, V.[Verónica],
Cruz-Barbosa, R.[Raúl],
Improving Bayesian Networks Breast Mass Diagnosis by Using Clinical
Data,
MCPR15(292-301).
Springer DOI
1506
BibRef
Dhungel, N.[Neeraj],
Carneiro, G.[Gustavo],
Bradley, A.P.[Andrew P.],
Automated Mass Detection in Mammograms Using Cascaded Deep Learning
and Random Forests,
DICTA15(1-8)
IEEE DOI
1603
BibRef
And:
Deep structured learning for mass segmentation from mammograms,
ICIP15(2950-2954)
IEEE DOI
1512
belief networks.
Mammograms; mass segmentation; structured inference; structured learning
BibRef
Guo, M.[Miao],
Dong, M.[Mev],
Wang, Z.[Zhaobev],
Ma, Y.[Yide],
Guo, Y.[Ya'nan],
A new method for mammographic mass segmentation based on parametric
active contour model,
ICWAPR15(27-33)
IEEE DOI
1511
cancer
BibRef
Molinara, M.[Mario],
Marrocco, C.[Claudio],
A Boosting-Based Approach to Refine the Segmentation of Masses in
Mammography,
CIAP13(II:572-580).
Springer DOI
1309
BibRef
Kim, D.H.[Dae Hoe],
Choi, J.Y.[Jae Young],
Ro, Y.M.[Yong Man],
A novel mammographic mass detection approach to combining suprevised
and unsuprevised detection algorithms,
ICIP12(2857-2860).
IEEE DOI
1302
BibRef
Hussain, M.[Muhammad],
Khan, S.[Salabat],
Muhammad, G.[Ghulam],
Bebis, G.N.[George N.],
Mass Detection in Digital Mammograms Using Optimized Gabor Filter Bank,
ISVC12(II: 82-91).
Springer DOI
1209
BibRef
Cheikhouhou, I.[Imene],
Djemal, K.[Khalifa],
Maaref, H.[Hichem],
Mass Description for Breast Cancer Recognition,
ICISP10(576-584).
Springer DOI
1006
BibRef
Sahba, F.[Farhang],
Venetsanopoulos, A.[Anastasios],
Mean shift based algorithm for mammographic breast mass detection,
ICIP10(3629-3632).
IEEE DOI
1009
BibRef
Cheikhrouhou, I.,
Djemal, K.,
Sellami, D.,
Maaref, H.,
Derbel, N.,
New mass description in mammographies,
IPTA08(1-5).
IEEE DOI
0811
BibRef
Wang, Y.[Ying],
Gao, X.B.[Xin-Bo],
Li, J.[Jie],
A Feature Analysis Approach to Mass Detection in Mammography Based on
RF-SVM,
ICIP07(V: 9-12).
IEEE DOI
0709
BibRef
Sampaio, W.B.,
Diniz, E.M.,
Silva, A.C.,
de Paiva, A.C.,
Detection of Masses in Mammograms Using Cellular Neural Networks,
Hidden Markov Models and Ripley's K Function,
WSSIP09(1-3).
IEEE DOI
0906
See also second-order analysis of stationary point processes, The.
BibRef
de Oliveira Martins, L.[Leonardo],
Junior, G.B.[Geraldo Braz],
da Silva, E.C.[Erick Corrêa],
Silva, A.C.[Aristófanes Corrêa],
Cardoso de Paiva, A.[Anselmo],
Classification of Breast Tissues in Mammogram Images Using Ripley's K
Function and Support Vector Machine,
ICIAR07(899-910).
Springer DOI
0708
See also second-order analysis of stationary point processes, The.
BibRef
Moayedi, F.[Fatemeh],
Azimifar, Z.[Zohreh],
Boostani, R.[Reza],
Katebi, S.[Serajodin],
Contourlet-Based Mammography Mass Classification,
ICIAR07(923-934).
Springer DOI
0708
BibRef
Oliver, A.[Arnau],
Lladó, X.[Xavier],
Martí, J.[Joan],
Martí, R.[Robert],
Freixenet, J.[Jordi],
False Positive Reduction in Breast Mass Detection Using Two-Dimensional
PCA,
IbPRIA07(II: 154-161).
Springer DOI
0706
BibRef
Mencattini, A.[Arianna],
Rabottino, G.[Giulia],
Salmeri, M.[Marcello],
Lojacono, R.[Roberto],
Colini, E.[Emanuele],
Breast Mass Segmentation in Mammographic Images by an Effective Region
Growing Algorithm,
ACIVS08(xx-yy).
Springer DOI
0810
BibRef
Oliver, A.[Arnau],
Marti, J.[Joan],
Marti, R.[Robert],
Bosch, A.[Anna],
Freixenet, J.[Jordi],
A new approach to the classification of mammographic masses and normal
breast tissue,
ICPR06(IV: 707-710).
IEEE DOI
0609
BibRef
Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Mammography, Thermal, Infrared Analysis .