21.13 Medical Applications -- Cancer Diagnosis and Analysis

Watanabe, S.[Sadakazu], and the CYBEST Group,
An automated apparatus for cancer prescreening: CYBEST,
CGIP(3), No. 4, December 1974, pp. 350-358.
Elsevier DOI 0501
BibRef

Moore, G.W.[G. William], Hutchins, G.M.[Grover M.], de la Monte, S.M.[Suzanne M.],
Lattice theory approach to metastatic disease patterns in autopsied human patients: Application to metastatic neuroblastoma,
PR(18), No. 2, 1985, pp. 91-102.
Elsevier DOI 0309
BibRef

Poulsen, R.S., Pedron, I.,
Region of Interest Finding in Reduced Resolution Color Imagery: Application to Cancer Cell Detection,
PR(28), No. 11, November 1995, pp. 1645-1655.
Elsevier DOI BibRef 9511

Zhu, Y.[Yan], Yan, Z.[Zhu],
Computerized tumor boundary detection using a Hopfield neural network,
MedImg(16), No. 1, February 1997, pp. 55-67.
IEEE Top Reference. 0205
BibRef

Clark, M.C., Hall, L.O., Goldgof, D.B., Velthuizen, R.P., Murtagh, F.R., Silbiger, M.S.,
Automatic Tumor Segmentation Using Knowledge-Based Techniques,
MedImg(17), No. 2, April 1998, pp. 187-201.
IEEE Top Reference. 9808
BibRef

Smallwood, R.H., Keshtkar, A., Wilkinson, B.A., Lee, J.A., Hamdy, F.C.,
Electrical impedance spectroscopy (EIS) in the urinary bladder: the effect of inflammation and edema on identification of malignancy,
MedImg(21), No. 6, June 2002, pp. 708-710.
IEEE Top Reference. 0208
BibRef

Liu, L., Bland, P.H., Williams, D.M., Schunck, B.G., Meyer, C.R.,
Application of robust sequential edge detection and linking to boundaries of low contrast lesions in medical images,
CVPR89(582-587).
IEEE DOI 0403
BibRef

Diaz, M.[Mireya], Rao, J. .S.I.[J. Sun-Il],
Non-parametric bootstrap ensembles for detection of tumor lesions,
PRL(28), No. 16, December 2007, pp. 2273-2283.
Elsevier DOI 0711
Statistical pattern recognition; Image analysis; Ensembles; Spatial correlation; Markov Random Fields; Unsupervised training BibRef

Gimi, B., Pathak, A.P., Ackerstaff, E., Glunde, K., Artemov, D., Bhujwalla, Z.M.,
Molecular Imaging of Cancer: Applications of Magnetic Resonance Methods,
PIEEE(93), No. 4, April 2005, pp. 784-799.
IEEE DOI 0504
BibRef

Thorne, S.H., Contag, C.H.,
Using in Vivo Bioluminescence Imaging to Shed Light on Cancer Biology,
PIEEE(93), No. 4, April 2005, pp. 750-762.
IEEE DOI 0504
BibRef

Zhou, B.[Bin], Xuan, J.H.[Jian-Hua], Wu, Q.R.[Qing-Rong], Wang, Y.[Yue],
3-D Deformation Guided On-Line Modification of Multi-leaf Collimators for Adaptive Radiation Therapy,
ICIAR08(xx-yy).
Springer DOI 0806
BibRef

Tommasi, T.[Tatiana], Orabona, F.[Francesco], Caputo, B.[Barbara],
Discriminative cue integration for medical image annotation,
PRL(29), No. 15, 1 November 2008, pp. 1996-2002.
Elsevier DOI 0811
Medical image annotation; Cue integration; Support vector machine
See also Learning methods for melanoma recognition. BibRef

Tang, J.S.[Jin-Shan], Rangayyan, R.[Raj], Yao, J.H.[Jian-Hua], Yang, Y.Y.[Yong-Yi],
Digital image processing and pattern recognition techniques for the detection of cancer,
PR(42), No. 6, June 2009, pp. 1015-1016.
Elsevier DOI 0902
BibRef

Li, F., Zhou, X., Ma, J., Wong, S.T.C.,
Multiple Nuclei Tracking Using Integer Programming for Quantitative Cancer Cell Cycle Analysis,
MedImg(29), No. 1, January 2010, pp. 96-105.
IEEE DOI 1001
BibRef

Taheri, S.[Sima], Ong, S.H.[Sim Heng], Chong, V.F.H.[Vincent F.H.],
Level-set segmentation of brain tumors using a threshold-based speed function,
IVC(28), No. 1, Januray 2010, pp. 26-37.
Elsevier DOI 1001
BibRef
Earlier:
Threshold-based 3D Tumor Segmentation using Level Set (TSL),
WACV07(45-45).
IEEE DOI 0702
3D segmentation; Threshold; Level-set BibRef

Konukoglu, E., Clatz, O., Menze, B.H., Stieltjes, B., Weber, M.A., Mandonnet, E., Delingette, H., Ayache, N.J.,
Image Guided Personalization of Reaction-Diffusion Type Tumor Growth Models Using Modified Anisotropic Eikonal Equations,
MedImg(29), No. 1, January 2010, pp. 77-95.
IEEE DOI 1001
BibRef

Huang, P.W.[Po-Whei], Lai, Y.H.[Yan-Hao],
Effective segmentation and classification for HCC biopsy images,
PR(43), No. 4, April 2010, pp. 1550-1563.
Elsevier DOI 1002
HCC biopsy image; Morphological grayscale reconstruction; k-nearest neighbor; Support vector machine; Feature selection; Decision-graph BibRef

Worz, S., Sander, P., Pfannmoller, M., Rieker, R.J., Joos, S., Mechtersheimer, G., Boukamp, P., Lichter, P., Rohr, K.,
3D Geometry-Based Quantification of Colocalizations in Multichannel 3D Microscopy Images of Human Soft Tissue Tumors,
MedImg(29), No. 8, August 2010, pp. 1474-1484.
IEEE DOI 1008
BibRef

Johnson, J.P., Krupinski, E.A., Yan, M., Roehrig, H., Graham, A.R., Weinstein, R.S.,
Using a Visual Discrimination Model for the Detection of Compression Artifacts in Virtual Pathology Images,
MedImg(30), No. 2, February 2011, pp. 306-314.
IEEE DOI 1102
BibRef

Weibel, T.[Thomas], Daul, C.[Christian], Wolf, D.[Didier], Rösch, R.[Ronald], Guillemin, F.[François],
Graph based construction of textured large field of view mosaics for bladder cancer diagnosis,
PR(45), No. 12, December 2012, pp. 4138-4150.
Elsevier DOI 1208
Image mosaicing; Seamless panoramic stitching; Image registration; Bladder cancer; Endoscopy; Graph cuts; Higher order terms; Non-linear refinement BibRef

Sun, Z.L.[Zhan-Li], Zheng, C.H.[Chun-Hou], Gao, Q.W.[Qing-Wei], Zhang, J.[Jun], Zhang, D.X.[De-Xiang],
Tumor Classification Using Eigengene-Based Classifier Committee Learning Algorithm,
SPLetters(19), No. 8, August 2012, pp. 455-458.
IEEE DOI 1208
BibRef

Li, X.L.[Xiu-Li], Chen, X.J.[Xin-Jian], Yao, J.H.[Jian-Hua], Zhang, X.[Xing], Yang, F.[Fei], Tian, J.[Jian],
Automatic Renal Cortex Segmentation Using Implicit Shape Registration and Novel Multiple Surfaces Graph Search,
MedImg(31), No. 10, October 2012, pp. 1849-1860.
IEEE DOI 1210
BibRef
And: MedImg(31), No. 12, December 2012, pp. 2366.
IEEE DOI 1212
BibRef

Martel, S.,
Journey to the center of a tumor,
Spectrum(49), No. 10, October 2012, pp. 48-53.
IEEE DOI 1210
BibRef

Ozdemir, E., Gunduz-Demir, C.,
A Hybrid Classification Model for Digital Pathology Using Structural and Statistical Pattern Recognition,
MedImg(32), No. 2, February 2013, pp. 474-483.
IEEE DOI 1301
BibRef

Song, Q., Bai, J., Han, D.F., Bhatia, S., Sun, W., Rockey, W., Bayouth, J.E., Buatti, J.M., Wu, X.D.,
Optimal Co-Segmentation of Tumor in PET-CT Images With Context Information,
MedImg(32), No. 9, 2013, pp. 1685-1697.
IEEE DOI 1309
Context information BibRef

Ventola, G.M.[Giovanna Maria], Colaprico, A.[Antonio], d'Angelo, F.[Fulvio], Colantuoni, V.[Vittorio],
An Approach to Identify miRNA Associated with Cancer Altered Pathways,
PR-PS-BB13(399-408).
Springer DOI 1309
BibRef

Heckel, F., Meine, H., Moltz, J.H., Kuhnigk, J.M., Heverhagen, J.T., Kiessling, A., Buerke, B., Hahn, H.K.,
Segmentation-Based Partial Volume Correction for Volume Estimation of Solid Lesions in CT,
MedImg(33), No. 2, February 2014, pp. 462-480.
IEEE DOI 1403
cancer BibRef

Rajaguru, H.[Harikumar], Bojan, V.K.[Vinoth Kumar],
Performance analysis of EM, SVD, and SVM classifiers in classification of carcinogenic regions of medical images,
IJIST(24), No. 1, 2014, pp. 16-22.
DOI Link 1403
EM, SVD, SVM, performance measures, quality metrics BibRef

Rajaguru, H.[Harikumar], Ganesan, K.[Karthick], Bojan, V.K.[Vinoth Kumar],
Earlier detection of cancer regions from MR image features and SVM classifiers,
IJIST(26), No. 3, 2016, pp. 196-208.
DOI Link 1609
MR images, segmentation, texture features, SVM BibRef

Huang, H.[Hu], Tosun, A.B.[Akif Burak], Guo, J.[Jia], Chen, C.[Cheng], Wang, W.[Wei], Ozolek, J.A.[John A.], Rohde, G.K.[Gustavo K.],
Cancer diagnosis by nuclear morphometry using spatial information,
PRL(42), No. 1, 2014, pp. 115-121.
Elsevier DOI 1404
Set classification BibRef

Adcock, A.[Aaron], Rubin, D.[Daniel], Carlsson, G.[Gunnar],
Classification of hepatic lesions using the matching metric,
CVIU(121), No. 1, 2014, pp. 36-42.
Elsevier DOI 1404
Medical image processing BibRef

Litjens, G., Debats, O., Barentsz, J., Karssemeijer, N., Huisman, H.,
Computer-Aided Detection of Prostate Cancer in MRI,
MedImg(33), No. 5, May 2014, pp. 1083-1092.
IEEE DOI 1405
Biopsy BibRef

Mitra, S., Shankar, B.U.,
Integrating Radio Imaging With Gene Expressions Toward a Personalized Management of Cancer,
HMS(44), No. 5, October 2014, pp. 664-677.
IEEE DOI 1411
biomedical MRI BibRef

Gangeh, M.J., Sadeghi-Naini, A., Diu, M., Tadayyon, H., Kamel, M.S., Czarnota, G.J.,
Categorizing Extent of Tumor Cell Death Response to Cancer Therapy Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy,
MedImg(33), No. 6, June 2014, pp. 1390-1400.
IEEE DOI 1407
Biomedical imaging BibRef

McCann, M.T., Ozolek, J.A., Castro, C.A., Parvin, B., Kovacevic, J.,
Automated Histology Analysis: Opportunities for signal processing,
SPMag(32), No. 1, January 2015, pp. 78-87.
IEEE DOI 1502
cancer BibRef

Chang, H.[Hang], Parvin, B.[Bahram],
Classification of 3D Multicellular Organization in Phase Microscopy for High Throughput Screening of Therapeutic Targets,
WACV15(436-441)
IEEE DOI 1503
Breast cancer BibRef

Chang, H.[Hang], Zhou, Y.[Yin], Borowsky, A.[Alexander], Barner, K.E.[Kenneth E.], Spellman, P.T.[Paul T.], Parvin, B.[Bahram],
Stacked Predictive Sparse Decomposition for Classification of Histology Sections,
IJCV(113), No. 1, May 2015, pp. 3-18.
Springer DOI 1506
BibRef

Zhou, Y.[Yin], Chang, H.[Hang], Barner, K.E.[Kenneth E.], Spellman, P.T.[Paul T.], Parvin, B.[Bahram],
Classification of Histology Sections via Multispectral Convolutional Sparse Coding,
CVPR14(3081-3088)
IEEE DOI 1409
BibRef

Chang, H.[Hang], Zhou, Y.[Yin], Spellman, P.T.[Paul T.], Parvin, B.[Bahram],
Stacked Predictive Sparse Coding for Classification of Distinct Regions in Tumor Histopathology,
ICCV13(169-176)
IEEE DOI 1403
BibRef

Chang, H.[Hang], Borowsky, A.[Alexander], Spellman, P.T.[Paul T.], Parvin, B.[Bahram],
Classification of Tumor Histology via Morphometric Context,
CVPR13(2203-2210)
IEEE DOI 1309
BibRef

Ginsburg, S.B., Lee, G., Ali, S., Madabhushi, A.,
Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology,
MedImg(35), No. 1, January 2016, pp. 76-88.
IEEE DOI 1601
Diseases BibRef

Siegert, Y., Jiang, X., Krieg, V., Bartholomäus, S.,
Classification-Based Record Linkage With Pseudonymized Data for Epidemiological Cancer Registries,
MultMed(18), No. 10, October 2016, pp. 1929-1941.
IEEE DOI 1610
cancer BibRef

Li, J.D.[Jun-Dong], Adilmagambetov, A.[Aibek], Jabbar, M.S.M.[Mohomed Shazan Mohomed], Zaïane, O.R.[Osmar R.], Osornio-Vargas, A.[Alvaro], Wine, O.[Osnat],
On discovering co-location patterns in datasets: A case study of pollutants and child cancers,
GeoInfo(20), No. 4, October 2016, pp. 651-692.
Springer DOI 1610
BibRef

Wang, H.[He], Feng, Y.M.[Yuan-Ming], Sa, Y.[Yu], Lu, J.Q.[Jun Q.], Ding, J.H.[Jun-Hua], Zhang, J.[Jun], Hu, X.H.[Xin-Hua],
Pattern recognition and classification of two cancer cell lines by diffraction imaging at multiple pixel distances,
PR(61), No. 1, 2017, pp. 234-244.
Elsevier DOI 1705
Single-cell assay BibRef

Li, L.[Laquan], Wang, J.[Jian], Lu, W.[Wei], Tan, S.[Shan],
Simultaneous tumor segmentation, image restoration, and blur kernel estimation in PET using multiple regularizations,
CVIU(155), No. 1, 2017, pp. 173-194.
Elsevier DOI 1702
Image restoration BibRef

Lapuyade-Lahorgue, J., Xue, J.H., Ruan, S.,
Segmenting Multi-Source Images Using Hidden Markov Fields With Copula-Based Multivariate Statistical Distributions,
IP(26), No. 7, July 2017, pp. 3187-3195.
IEEE DOI 1706
Bayes methods, Fuses, Hidden Markov models, Image segmentation, Magnetic resonance imaging, Probabilistic logic, Tumors, Bayesian inference, Data fusion, copulas, hidden Markov fields, multi-source images, tumor, segmentation BibRef

Guo, H., He, X., Liu, M., Zhang, Z., Hu, Z., Tian, J.,
Weight Multispectral Reconstruction Strategy for Enhanced Reconstruction Accuracy and Stability With Cerenkov Luminescence Tomography,
MedImg(36), No. 6, June 2017, pp. 1337-1346.
IEEE DOI 1706
Image reconstruction, Optical imaging, Optical scattering, Photonics, Tomography, Tumors, Cerenkov luminescence tomography, Weight multispectral reconstruction, inverse, problem BibRef

Carneiro, G., Peng, T., Bayer, C., Navab, N.,
Automatic Quantification of Tumour Hypoxia From Multi-Modal Microscopy Images Using Weakly-Supervised Learning Methods,
MedImg(36), No. 7, July 2017, pp. 1405-1417.
IEEE DOI 1707
Biomedical imaging, Cancer, Computational modeling, Manuals, Medical treatment, Training, Tumors, Microscopy, deep learning, high-order loss functions, structured output learning, weakly-supervised, training BibRef

Jia, Z., Huang, X., Chang, E.I.C., Xu, Y.,
Constrained Deep Weak Supervision for Histopathology Image Segmentation,
MedImg(36), No. 11, November 2017, pp. 2376-2388.
IEEE DOI 1711
Cancer, Neural networks, Prediction algorithms, Convolutional neural networks, BibRef

Wei, J.[Jie], Zhang, L.[Lin], Fu, B.M.[Bingmei M.],
Automatic Quantification of Endothelial Nitric Oxide Levels in a Microvessel with and without Tumor Cell Adhesion,
IJIG(18), No. 01, 2018, pp. 1850001.
DOI Link 1801
BibRef

Zhang, L., Lu, L., Summers, R.M., Kebebew, E., Yao, J.,
Convolutional Invasion and Expansion Networks for Tumor Growth Prediction,
MedImg(37), No. 2, February 2018, pp. 638-648.
IEEE DOI 1802
Computer architecture, Mathematical model, Microprocessors, Predictive models, Sociology, Statistics, Tumors, Tumor growth prediction BibRef

Roque, T., Risser, L., Kersemans, V., Smart, S., Allen, D., Kinchesh, P., Gilchrist, S., Gomes, A.L., Schnabel, J.A., Chappell, M.A.,
A DCE-MRI Driven 3-D Reaction-Diffusion Model of Solid Tumor Growth,
MedImg(37), No. 3, March 2018, pp. 724-732.
IEEE DOI 1804
biomedical MRI, cancer, cellular biophysics, image enhancement, medical image processing, parameter estimation, tissue modelling BibRef

Hu, Z.L.[Zi-Long], Tang, J.S.[Jin-Shan], Wang, Z.M.[Zi-Ming], Zhang, K.[Kai], Zhang, L.[Ling], Sun, Q.L.[Qing-Ling],
Deep learning for image-based cancer detection and diagnosis: A survey,
PR(83), 2018, pp. 134-149.
Elsevier DOI 1808
Survey, Cancer Detection. BibRef

Zheng, Y., Jiang, Z., Zhang, H., Xie, F., Ma, Y., Shi, H., Zhao, Y.,
Histopathological Whole Slide Image Analysis Using Context-Based CBIR,
MedImg(37), No. 7, July 2018, pp. 1641-1652.
IEEE DOI 1808
cancer, content-based retrieval, image classification, image representation, image retrieval, medical image processing, contextual information BibRef

Hernández-Cabronero, M., Sanchez, V., Blanes, I., Aulí-Llinàs, F., Marcellin, M.W., Serra-Sagristà, J.,
Mosaic-Based Color-Transform Optimization for Lossy and Lossy-to-Lossless Compression of Pathology Whole-Slide Images,
MedImg(38), No. 1, January 2019, pp. 21-32.
IEEE DOI 1901
Transforms, Image color analysis, Image coding, Optimization, Transform coding, Pathology, Compression algorithms, image compression BibRef

Arunachalam, M.[Murugan], Savarimuthu, S.R.[Sabeenian Royappan],
A novel prognosis and segmentation of necrosis (dead cells) in contrast enhanced T1-weighted glioblastoma tumor with automatic contextual clustering,
IJIST(29), No. 1, March 2019, pp. 65-76.
WWW Link. 1902
BibRef

Castilla, C., Maška, M., Sorokin, D.V., Meijering, E., Ortiz-de-Solórzano, C.,
3-D Quantification of Filopodia in Motile Cancer Cells,
MedImg(38), No. 3, March 2019, pp. 862-872.
IEEE DOI 1903
Image segmentation, Biomedical imaging, Fluorescence, Manuals, Cancer, Microscopy, deep learning BibRef

Passos, L.A.[Leandro A.], de Souza, Jr., L.A.[Luis A.], Mendel, R.[Robert], Ebigbo, A.[Alanna], Probst, A.[Andreas], Messmann, H.[Helmut], Palm, C.[Christoph], Papa, J.P.[João Paulo],
Barrett's esophagus analysis using infinity Restricted Boltzmann Machines,
JVCIR(59), 2019, pp. 475-485.
Elsevier DOI 1903
Pre-cancer diagnosis. Barrett's esophagus, Infinity Restricted Boltzmann Machines, Meta-heuristics, Deep learning BibRef

Yang, X.H.[Xiao-Hui], Wu, W.M.[Wen-Ming], Chen, Y.M.[Yun-Mei], Li, X.Q.[Xian-Qi], Zhang, J.[Juan], Long, D.[Dan], Yang, L.J.[Li-Jun],
An integrated inverse space sparse representation framework for tumor classification,
PR(93), 2019, pp. 293-311.
Elsevier DOI 1906
Tumor classification, Microarray gene expression data, Decision information genes, Inverse space sparse representation BibRef

Afshar, P., Mohammadi, A., Plataniotis, K.N., Oikonomou, A., Benali, H.,
From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and opportunities,
SPMag(36), No. 4, July 2019, pp. 132-160.
IEEE DOI 1907
Cancer, Feature extraction, Tumors, Predictive models, Medical diagnostic imaging, Deep learning, Machine learning BibRef

Sukhia, K.N.[Komal Nain], Ghafoor, A.[Abdul], Riaz, M.M.[Muhammad Mohsin], Iltaf, N.[Naima],
Automated acute lymphoblastic leukaemia detection system using microscopic images,
IET-IPR(13), No. 13, November 2019, pp. 2548-2553.
DOI Link 1911
BibRef

Al-Tahhan, F.E., Sakr, A.A.[Ali A.], Aladle, D.A.[Doaa A.], Fares, M.E.,
Improved image segmentation algorithms for detecting types of acute lymphatic leukaemia,
IET-IPR(13), No. 13, November 2019, pp. 2595-2603.
DOI Link 1911
BibRef

Shao, W., Han, Z., Cheng, J., Cheng, L., Wang, T., Sun, L., Lu, Z., Zhang, J., Zhang, D., Huang, K.,
Integrative Analysis of Pathological Images and Multi-Dimensional Genomic Data for Early-Stage Cancer Prognosis,
MedImg(39), No. 1, January 2020, pp. 99-110.
IEEE DOI 2001
Feature extraction, Cancer, Genomics, Bioinformatics, Prognostics and health management, DNA, Histopathological images, ordinal multi-model feature selection BibRef

Barstugan, M.[Mucahid], Ceylan, R.[Rahime], Asoglu, S.[Semih], Cebeci, H.[Hakan], Koplay, M.[Mustafa],
Adrenal tumor characterization on magnetic resonance images,
IJIST(30), No. 1, 2020, pp. 252-265.
DOI Link 2002
adrenal glands, adrenal tumor classification, feature extraction, MR images, segmentation BibRef

Sharif, M.I.[Muhammad Irfan], Li, J.P.[Jian Ping], Naz, J.[Javeria], Rashid, I.[Iqra],
A comprehensive review on multi-organs tumor detection based on machine learning,
PRL(131), 2020, pp. 30-37.
Elsevier DOI 2004
Classification, CT, Feature extraction, MRI, Pre-processing BibRef

Zhang, L., Lu, L., Wang, X., Zhu, R.M., Bagheri, M., Summers, R.M., Yao, J.,
Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data,
MedImg(39), No. 4, April 2020, pp. 1114-1126.
IEEE DOI 2004
Tumors, Image segmentation, Predictive models, Mathematical model, Computed tomography, 4D medical imaging BibRef

Kurmi, Y.[Yashwant], Chaurasia, V.[Vijayshri], Ganesh, N.[Narayanan], Kesharwani, A.[Abhimanyu],
Microscopic images classification for cancer diagnosis,
SIViP(14), No. 4, June 2020, pp. 665-673.
Springer DOI 2005
BibRef

Agarwal, M.[Monika], Rani, G.[Geeta], Dhaka, V.S.[Vijaypal Singh],
Optimized contrast enhancement for tumor detection,
IJIST(30), No. 3, 2020, pp. 687-703.
DOI Link 2008
adaptive entropy, contrast, histogram weighted, optimum, Otsu's double threshold, particle swarm optimization, tumor BibRef

Gupta, T.[Tanvi], Gandhi, T.K.[Tapan K.], Gupta, R.K., Panigrahi, B.K.,
Classification of patients with tumor using MR FLAIR images,
PRL(139), 2020, pp. 112-117.
Elsevier DOI 2011
MRI, SVM, FLAIR, Tumor BibRef

Kollem, S.[Sreedhar], Reddy, K.R.L.[Katta Rama Linga], Rao, D.S.[Duggirala Srinivasa],
Modified transform-based gamma correction for MRI tumor image denoising and segmentation by optimized histon-based elephant herding algorithm,
IJIST(30), No. 4, 2020, pp. 1271-1293.
DOI Link 2011
Atanassov intuitionistic fuzzy set, elephant herding algorithm, generalized cross-validation, transform-based gamma correction BibRef

Scheufele, K., Subramanian, S., Biros, G.,
Fully Automatic Calibration of Tumor-Growth Models Using a Single mpMRI Scan,
MedImg(40), No. 1, January 2021, pp. 193-204.
IEEE DOI 2012
Tumors, Calibration, Mathematical model, Biological system modeling, Robustness, Biomarkers, tumor initiation BibRef

Liu, Y., Yin, M., Sun, S.,
DetexNet: Accurately Diagnosing Frequent and Challenging Pediatric Malignant Tumors,
MedImg(40), No. 1, January 2021, pp. 395-404.
IEEE DOI 2012
Feature extraction, Tumors, Pathology, Cancer, Neural networks, Task analysis, Pediatrics, Assistant pathology diagnosis, pediatric cancer BibRef

Abdulla, A.A.[Alan Anwer],
Efficient computer-aided diagnosis technique for leukaemia cancer detection,
IET-IPR(14), No. 17, 24 December 2020, pp. 4435-4440.
DOI Link 2104
BibRef

Kandan, R.S.[Rathinam Somas], Murugeswari, M.[Muthuvel],
Performance enhancement of image segmentation analysis for multi-grade tumour classification in MRI image,
IET-IPR(14), No. 17, 24 December 2020, pp. 4477-4485.
DOI Link 2104
BibRef

Yang, X.H.[Xiao-Hui], Wu, W.[Wenming], Jiao, L.C.[Li-Cheng], Jiao, C.Z.[Chang-Zhe], Jiao, Z.C.[Zhi-Cheng],
A deep fusion framework for unlabeled data-driven tumor recognition,
PR(119), 2021, pp. 108066.
Elsevier DOI 2108
Unlabeled data, Deep representation learning, Non-negative matrix factorization, Tumor recognition BibRef

Meenachi, L., Ramakrishnan, S.,
Metaheuristic Search Based Feature Selection Methods for Classification of Cancer,
PR(119), 2021, pp. 108079.
Elsevier DOI 2108
Ant Colony Optimization, Genetic Algorithm, Tabu Search, Fuzzy Rough set, Optimal feature selection BibRef

Liu, S.T.[Shu-Ting], Zhang, B.C.[Bao-Chang], Liu, Y.Q.[Yi-Qing], Han, A.[Anjia], Shi, H.J.[Hui-Juan], Guan, T.[Tian], He, Y.H.[Yong-Hong],
Unpaired Stain Transfer Using Pathology-Consistent Constrained Generative Adversarial Networks,
MedImg(40), No. 8, August 2021, pp. 1977-1989.
IEEE DOI 2108
Cancer, Generative adversarial networks, Task analysis, Tumors, Image analysis, Histopathology, Annotations, Histopathology, hematoxylin-eosin (H&E) BibRef

Sinthia, P., Malathi, M.,
Cancer detection using convolutional neural network optimized by multistrategy artificial electric field algorithm,
IJIST(31), No. 3, 2021, pp. 1386-1403.
DOI Link 2108
cancer detection, convolutional neural network, ensemble learning, hyper-parameter optimization, multistrategy artificial electric field algorithm BibRef

Seo, H.S.[Hyun-Seok], Yu, L.[Lequan], Ren, H.Y.[Hong-Yi], Li, X.M.[Xiao-Meng], Shen, L.Y.[Li-Yue], Xing, L.[Lei],
Deep Neural Network With Consistency Regularization of Multi-Output Channels for Improved Tumor Detection and Delineation,
MedImg(40), No. 12, December 2021, pp. 3369-3378.
IEEE DOI 2112
Image segmentation, Task analysis, Tumors, Training, Biomedical imaging, Neural networks, Feature extraction, segmentation BibRef

Devendran, M.[Menaga], Sathya, R.[Revathi],
An approach for cancer classification using optimization driven deep learning,
IJIST(31), No. 4, 2021, pp. 1936-1953.
DOI Link 2112
cancer classification, deep learning, fractional calculus, gene expression data, optimization BibRef

Li, X.J.[Xiao-Jie], Tang, M.X.[Ming-Xuan], Guo, F.[Feng], Li, Y.X.[Yuan-Xi], Cao, K.L.[Kun-Ling], Song, Q.[Qi], Wu, X.[Xi], Sun, S.[Shanhui], Zhou, J.[Jiliu],
DDNet: 3D densely connected convolutional networks with feature pyramids for nasopharyngeal carcinoma segmentation,
IET-IPR(16), No. 1, 2022, pp. 39-48.
DOI Link 2112
BibRef

Ning, Z.Y.[Zhen-Yuan], Du, D.H.[Deng-Hui], Tu, C.[Chao], Feng, Q.J.[Qian-Jin], Zhang, Y.[Yu],
Relation-Aware Shared Representation Learning for Cancer Prognosis Analysis with Auxiliary Clinical Variables and Incomplete Multi-Modality Data,
MedImg(41), No. 1, January 2022, pp. 186-198.
IEEE DOI 2201
Cancer, Prognostics and health management, Data models, Training, Genomics, Clinical diagnosis, Bioinformatics, Prognosis analysis, incomplete multi-modality data
See also Relation-Induced Multi-Modal Shared Representation Learning for Alzheimer's Disease Diagnosis. BibRef

Chen, R.J.[Richard J.], Lu, M.Y.[Ming Y.], Wang, J.W.[Jing-Wen], Williamson, D.F.K.[Drew F. K.], Rodig, S.J.[Scott J.], Lindeman, N.I.[Neal I.], Mahmood, F.[Faisal],
Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis,
MedImg(41), No. 4, April 2022, pp. 757-770.
IEEE DOI 2204
Bioinformatics, Genomics, Feature extraction, Cancer, Tumors, Machine learning, Microprocessors, Multimodal learning, survival analysis BibRef

Valanarasu, J.M.J.[Jeya Maria Jose], Sindagi, V.A.[Vishwanath A.], Hacihaliloglu, I.[Ilker], Patel, V.M.[Vishal M.],
KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation,
MedImg(41), No. 4, April 2022, pp. 965-976.
IEEE DOI 2204
Image segmentation, Feature extraction, Tumors, Convolution, Medical diagnostic imaging, overcomplete representations BibRef

Shi, S.L.[Shao-Long], Chen, Y.F.[Yi-Fan], Yao, X.[Xin],
NGA-Inspired Nanorobots-Assisted Detection of Multifocal Cancer,
Cyber(52), No. 5, May 2022, pp. 2787-2797.
IEEE DOI 2206
Tumors, Nanobioscience, Cancer detection, Cancer, Genetic algorithms, Blood, Magnetic resonance imaging, Cancer detection, niche genetic algorithm (NGA) BibRef

Wang, H.[Han], Yi, F.S.[Fa-Sheng], Wang, J.L.[Jing-Ling], Yi, Z.[Zhang], Zhang, H.X.[Hai-Xian],
RECISTSup: Weakly-Supervised Lesion Volume Segmentation Using RECIST Measurement,
MedImg(41), No. 7, July 2022, pp. 1849-1861.
IEEE DOI 2207
Lesions, Image segmentation, Annotations, Volume measurement, Training, Loss measurement, Lesion segmentation, deep convolutional neural networks BibRef

Lai, H.R.[Hao-Ran], Fu, S.[Sirui], Zhang, J.[Jie], Cao, J.Y.[Jian-Yun], Feng, Q.J.[Qian-Jin], Lu, L.[Ligong], Huang, M.[Meiyan],
Prior Knowledge-Aware Fusion Network for Prediction of Macrovascular Invasion in Hepatocellular Carcinoma,
MedImg(41), No. 10, October 2022, pp. 2644-2657.
IEEE DOI 2210
Tumors, Feature extraction, Computed tomography, Hospitals, Data mining, Lesions, Radiomics, Hepatocellular carcinoma, rotation invariance BibRef

Yan, K.[Ke], Cai, J.Z.[Jin-Zheng], Jin, D.[Dakai], Miao, S.[Shun], Guo, D.[Dazhou], Harrison, A.P.[Adam P.], Tang, Y.[Youbao], Xiao, J.[Jing], Lu, J.J.[Jing-Jing], Lu, L.[Le],
SAM: Self-Supervised Learning of Pixel-Wise Anatomical Embeddings in Radiological Images,
MedImg(41), No. 10, October 2022, pp. 2658-2669.
IEEE DOI 2210
Task analysis, Computed tomography, X-rays, Training, Lesions, Prediction algorithms, Contrastive learning, self-supervised learning BibRef

Hossain, M.M.[Md Murad], Konofagou, E.E.[Elisa E.],
Imaging of Single Transducer-Harmonic Motion Imaging-Derived Displacements at Several Oscillation Frequencies Simultaneously,
MedImg(41), No. 11, November 2022, pp. 3099-3115.
IEEE DOI 2211
Imaging, Tumors, Harmonic analysis, Ultrasonic imaging, Frequency modulation, Oscillators, Elastography, high-frequency ARF BibRef

Qiao, P.C.[Peng-Chong], Li, H.[Han], Song, G.[Guoli], Han, H.[Hu], Gao, Z.Q.[Zhi-Qiang], Tian, Y.H.[Yong-Hong], Liang, Y.S.[Yong-Sheng], Li, X.[Xi], Zhou, S.K.[S. Kevin], Chen, J.[Jie],
Semi-Supervised CT Lesion Segmentation Using Uncertainty-Based Data Pairing and SwapMix,
MedImg(42), No. 5, May 2023, pp. 1546-1562.
IEEE DOI 2305
Lesions, Image segmentation, Computed tomography, Uncertainty, Training, Predictive models, Data models, Semi-supervised learning, unreliable pseudo labels BibRef

Chang, S.J.[Shao-Jie], Gao, Y.F.[Yong-Feng], Pomeroy, M.J.[Marc J.], Bai, T.[Ti], Zhang, H.[Hao], Lu, S.[Siming], Pickhardt, P.J.[Perry J.], Gupta, A.[Amit], Reiter, M.J.[Michael J.], Gould, E.S.[Elaine S.], Liang, Z.R.[Zheng-Rong],
Exploring Dual-Energy CT Spectral Information for Machine Learning-Driven Lesion Diagnosis in Pre-Log Domain,
MedImg(42), No. 6, June 2023, pp. 1835-1845.
IEEE DOI 2306
Lesions, Cancer, Computed tomography, Image reconstruction, Feature extraction, Convolutional neural networks, Attenuation, malignant and benign differentiation BibRef

Li, Y.L.[Yun-Ling], Li, S.X.[Shang-Xuan], Ju, H.Q.[Han-Qiu], Harada, T.[Tatsuya], Zhang, H.L.[Hong-Lai], Duan, T.[Ting], Wang, G.Y.[Guang-Yi], Zhang, L.J.[Li-Juan], Gu, L.[Lin], Zhou, W.[Wu],
Correlated and individual feature learning with contrast-enhanced MR for malignancy characterization of hepatocellular carcinoma,
PR(142), 2023, pp. 109638.
Elsevier DOI 2307
Multimodal fusion, Hepatocellular carcinoma, Deep feature, Malignancy characterization, Contrast-enhanced MR BibRef

Zhan, G.[Gan], Wang, F.[Fang], Wang, W.B.[Wei-Bin], Li, Y.[Yinhao], Chen, Q.Q.[Qing-Qing], Hu, H.J.[Hong-Jie], Chen, Y.W.[Yen-Wei],
A Transformer-based Model for Preoperative Early Recurrence Prediction of Hepatocellular Carcinoma with Muti-modality Mri,
MLCSA22(185-194).
Springer DOI 2307
BibRef

Jimenez-Sanchez, D.[Daniel], Ariz, M.[Mikel], de Andrea, C.E.[Carlos E.], Ortiz-De-Solórzano, C.[Carlos],
Synplex: In Silico Modeling of the Tumor Microenvironment From Multiplex Images,
MedImg(42), No. 10, October 2023, pp. 3048-3058.
IEEE DOI 2310
BibRef

Zhang, Y.[Yue], Peng, C.T.[Cheng-Tao], Tong, R.F.[Ruo-Feng], Lin, L.[Lanfen], Chen, Y.W.[Yen-Wei], Chen, Q.Q.[Qing-Qing], Hu, H.J.[Hong-Jie], Zhou, S.K.[S. Kevin],
Multi-Modal Tumor Segmentation With Deformable Aggregation and Uncertain Region Inpainting,
MedImg(42), No. 10, October 2023, pp. 3091-3103.
IEEE DOI 2310
BibRef

Xie, Y.T.[Yu-Tong], Zhang, J.P.[Jian-Peng], Xia, Y.[Yong], Shen, C.H.[Chun-Hua],
Learning From Partially Labeled Data for Multi-Organ and Tumor Segmentation,
PAMI(45), No. 12, December 2023, pp. 14905-14919.
IEEE DOI 2311
BibRef
Earlier: A2, A1, A3, A4:
DoDNet: Learning to Segment Multi-Organ and Tumors from Multiple Partially Labeled Datasets,
CVPR21(1195-1204)
IEEE DOI 2111
Image segmentation, Head, Annotations, Encoding, Labeling, Task analysis BibRef

Zhang, J.H.[Jin-Hong], Li, B.[Bin], Qiu, Q.H.[Qian-Hui], Mo, H.Q.[Hong-Qiang], Tian, L.F.[Lian-Fang],
SICNet: Learning selective inter-slice context via Mask-Guided Self-knowledge distillation for NPC segmentation,
JVCIR(98), 2024, pp. 104053.
Elsevier DOI 2402
Nasopharyngeal carcinoma, Segmentation, Convolutional neural networks, Self-knowledge distillation, Selective inter-slice context BibRef

Hayashi, T.[Tatsuya], Ito, N.[Naoki], Tabata, K.[Koji], Nakamura, A.[Atsuyoshi], Fujita, K.[Katsumasa], Harada, Y.[Yoshinori], Komatsuzaki, T.[Tamiki],
Gaussian process classification bandits,
PR(149), 2024, pp. 110224.
Elsevier DOI 2403
Motivative application is fast cancer diagnosis by Raman spectra. Bandit problem, Gaussian process, Classification bandits, Level set estimation BibRef

Ding, N.[Ning], Bao, X.[Xu], Sun, S.[Shantong], Wang, Y.[Yun],
High-precision real-time urine crystallization recognition based on dilated bilinear space pyramid ConvNext,
IJIST(34), No. 2, 2024, pp. e22999.
DOI Link 2402
bilinear, crystalluria detection, deep learning, fine-grained, loss function, object detection BibRef

Wang, P.Y.[Peng-Yu], Zhang, H.Q.[Hua-Qi], Zhu, M.[Meilu], Jiang, X.[Xi], Qin, J.[Jing], Yuan, Y.X.[Yi-Xuan],
MGIML: Cancer Grading With Incomplete Radiology-Pathology Data via Memory Learning and Gradient Homogenization,
MedImg(43), No. 6, June 2024, pp. 2113-2124.
IEEE DOI 2406
Pathology, Radiology, Cancer, Optimization, Training, Task analysis, Feature extraction, Incomplete multi-modal learning, gradient homogenization BibRef

Leng, J.[Jiake], Zhang, Y.[Yiyan], Liu, X.[Xiang], Cui, Y.M.[Yi-Ming], Zhao, J.[Junhan], Ge, Y.X.[Yong-Xin],
Error-Robust and Label-Efficient Deep Learning for Understanding Tumor Microenvironment From Spatial Transcriptomics,
CirSysVideo(34), No. 8, August 2024, pp. 6785-6796.
IEEE DOI 2408
Gene expression, Deep learning, Noise measurement, Uncertainty, Sequential analysis, Cancer, Data models, Tumor microenvironment, label efficiency BibRef

Zhou, T.X.[Tong-Xue],
M2GCNet: Multi-Modal Graph Convolution Network for Precise Brain Tumor Segmentation Across Multiple MRI Sequences,
IP(33), 2024, pp. 4896-4910.
IEEE DOI 2409
Tumors, Convolution, Image segmentation, Brain modeling, Magnetic resonance imaging, Decoding, Correlation, deep learning BibRef

Dong, Q.H.[Qi-Hua], Du, H.[Hao], Song, Y.[Ying], Xu, Y.[Yan], Liao, J.[Jing],
Preserving Tumor Volumes for Unsupervised Medical Image Registration,
ICCV23(21151-21161)
IEEE DOI Code:
WWW Link. 2401
BibRef

Liu, J.[Jie], Zhang, Y.X.[Yi-Xiao], Chen, J.N.[Jie-Neng], Xiao, J.F.[Jun-Fei], Lu, Y.Y.[Yong-Yi], Landman, B.A.[Bennett A.], Yuan, Y.X.[Yi-Xuan], Yuille, A.L.[Alan L.], Tang, Y.C.[Yu-Cheng], Zhou, Z.[Zongwei],
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection,
ICCV23(21095-21107)
IEEE DOI 2401
BibRef

Rohail, K.[Kinza], Bashir, S.[Saba], Ali, H.[Hazrat], Alam, T.[Tanvir], Khan, S.[Sheheryar], Wu, J.[Jia], Chen, P.J.[Ping-Jun], Qureshi, R.[Rizwan],
Understanding Tumor Micro Environment Using Graph Theory,
ACCVWS22(90-101).
Springer DOI 2307
BibRef

Ling, Z.Q.[Zi-Qin], Tao, G.H.[Gui-Hua], Li, Y.[Yang], Cai, H.M.[Hong-Min],
NPCFORMER: Automatic Nasopharyngeal Carcinoma Segmentation Based on Boundary Attention and Global Position Context Attention,
ICIP22(1981-1985)
IEEE DOI 2211
Deep learning, Image segmentation, Computational modeling, Malignant tumors, Transformers, Lesions, Context modeling, Transformer BibRef

Basu, S.[Soumen], Gupta, M.[Mayank], Rana, P.[Pratyaksha], Gupta, P.[Pankaj], Arora, C.[Chetan],
Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning,
CVPR22(20854-20864)
IEEE DOI 2210
Visualization, Technological innovation, Ultrasonic imaging, Supervised learning, Computer architecture, Object detection, Vision applications and systems BibRef

Horvath, I.[Izabela], Paetzold, J.[Johannes], Schoppe, O.[Oliver], Al-Maskari, R.[Rami], Ezhov, I.[Ivan], Shit, S.[Suprosanna], Li, H.W.[Hong-Wei], Ertürk, A.[Ali], Menze, B.[Bjoern],
METGAN: Generative Tumour Inpainting and Modality Synthesis in Light Sheet Microscopy,
WACV22(3230-3240)
IEEE DOI 2202
Training, Image segmentation, Image resolution, Microscopy, Semantics, Focusing, Generators, Grouping and Shape BibRef

Cornelissen, S., van der Putten, J.A., Boers, T.G.W., Jukema, J.B., Fockens, K.N., Bergman, J.J.G.H.M., van der Sommen, F., de With, P.H.N.,
Evaluating Self-Supervised Learning Methods for Downstream Classification of Neoplasia in Barrett's Esophagus,
ICIP21(66-70)
IEEE DOI 2201
Learning systems, Training, Hospitals, Shape, Superresolution, Machine learning, Data models, representation learning, endoscopy BibRef

Barmpoutis, P.[Panagiotis], Kayhanian, H.[Hamzeh], Waddingham, W.[William], Alexander, D.C.[Daniel C.], Jansen, M.[Marnix],
Three-dimensional tumour microenvironment reconstruction and tumour-immune interactions' analysis,
DICTA21(01-06)
IEEE DOI 2201
Multiplexing, Solid modeling, Adaptation models, Pathology, Computational modeling, Spatial resolution, immune subpopulations BibRef

Putzu, L.[Lorenzo], Untesco, M.[Maxim], Fumera, G.[Giorgio],
Automatic Myelofibrosis Grading from Silver-Stained Images,
CAIP21(I:195-205).
Springer DOI 2112
BibRef

Welikala, R.A.[Roshan Alex], Remagnino, P.[Paolo], Lim, J.H.[Jian Han], Chan, C.S.[Chee Seng], Rajendran, S.[Senthilmani], Kallarakkal, T.G.[Thomas George], Zain, R.B.[Rosnah Binti], Jayasinghe, R.D.[Ruwan Duminda], Rimal, J.[Jyotsna], Kerr, A.R.[Alexander Ross], Amtha, R.[Rahmi], Patil, K.[Karthikeya], Tilakaratne, W.M.[Wanninayake Mudiyanselage], Cheong, S.C.[Sok Ching], Barman, S.A.[Sarah Ann],
Clinically Guided Trainable Soft Attention for Early Detection of Oral Cancer,
CAIP21(I:226-236).
Springer DOI 2112
BibRef

Losquadro, C.[Chiara], Conforto, S.[Silvia], Schmid, M.[Maurizio], Giunta, G.[Gaetano], Rengo, M.[Marco], Cardinale, V.[Vincenzo], Carpino, G.[Guido], Laghi, A.[Andrea], Lleo, A.[Ana], Muglia, R.[Riccardo], Lanza, E.[Ezio], Torzilli, G.[Guido],
Small and Large Bile Ducts Intrahepatic Cholangiocarcinoma Classification: A Preliminary Feature-Based Study,
CAIP21(I:237-244).
Springer DOI 2112
BibRef

Chao, S.[Sherry], Belanger, D.[David],
Generalizing Few-Shot Classification of Whole-Genome Doubling Across Cancer Types,
CVAMD21(3375-3385)
IEEE DOI 2112
Deep learning, Histopathology, Neural networks, Real-time systems, Task analysis BibRef

Jonnalagedda, P.[Padmaja], Weinberg, B.[Brent], Allen, J.[Jason], Min, T.L.[Taejin L.], Bhanu, S.[Shiv], Bhanu, B.[Bir],
SAGE: Sequential Attribute Generator for Analyzing Glioblastomas Using Limited Dataset,
ICPR21(4941-4948)
IEEE DOI 2105
Visualization, Pipelines, Biomarkers, Streaming media, Generative adversarial networks, Generators, Task analysis BibRef

Daoud, B.[Bilel], Morooka, K.[Ken'ichi], Miyauchi, S.[Shoko], Kurazume, R.[Ryo], Mnejja, W.[Wafa], Farhat, L.[Leila], Daoud, J.[Jamel],
A Deep Learning-Based Method for Predicting Volumes of Nasopharyngeal Carcinoma for Adaptive Radiation Therapy Treatment,
ICPR21(3256-3263)
IEEE DOI 2105
Learning systems, Adaptive systems, Graphical models, Computed tomography, CT images BibRef

Rundo, F.[Francesco], Banna, G.L.[Giuseppe Luigi], Trenta, F.[Francesca], Spampinato, C.[Concetto], Bidaut, L.[Luc], Ye, X.[Xujiong], Kollias, S.[Stefanos], Battiato, S.[Sebastiano],
Advanced Non-linear Generative Model with a Deep Classifier for Immunotherapy Outcome Prediction: A Bladder Cancer Case Study,
AIHA20(227-242).
Springer DOI 2103
BibRef

Tokunaga, H.[Hiroki], Iwana, B.K.[Brian Kenji], Teramoto, Y.[Yuki], Yoshizawa, A.[Akihiko], Bise, R.[Ryoma],
Negative Pseudo Labeling Using Class Proportion for Semantic Segmentation in Pathology,
ECCV20(XV:430-446).
Springer DOI 2011
BibRef

Uehara, K.[Kazuki], Murakawa, M.[Masahiro], Nosato, H.[Hirokazu], Sakanashi, H.[Hidenori],
Explainable Feature Embedding using Convolutional Neural Networks for Pathological Image Analysis,
ICPR21(4560-4565)
IEEE DOI 2105
BibRef
Earlier:
Multi-Scale Explainable Feature Learning for Pathological Image Analysis Using Convolutional Neural Networks,
ICIP20(1931-1935)
IEEE DOI 2011
Pathology, Visualization, Solid modeling, Dictionaries, Image analysis, Vector quantization, Receivers, Explainable AI, Pathological images. Feature extraction, Training, Dictionaries, Decoding, Neural networks, Hospitals, Explainable AI, Pathological images, Convolutional neural networks BibRef

Guo, D., Jin, D., Zhu, Z., Ho, T., Harrison, A.P., Chao, C., Xiao, J., Lu, L.,
Organ at Risk Segmentation for Head and Neck Cancer Using Stratified Learning and Neural Architecture Search,
CVPR20(4222-4231)
IEEE DOI 2008
Cancer, Image segmentation, Computer architecture, Shape, Neck BibRef

Hashimoto, N., Fukushima, D., Koga, R., Takagi, Y., Ko, K., Kohno, K., Nakaguro, M., Nakamura, S., Hontani, H., Takeuchi, I.,
Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images,
CVPR20(3851-3860)
IEEE DOI 2008
Cancer, Tumors, Image color analysis, Pathology, Training, Feature extraction, Hospitals BibRef

Hering, J.[Jan], Kybic, J.[Jan],
Generalized Multiple Instance Learning for Cancer Detection in Digital Histopathology,
ICIAR20(II:274-282).
Springer DOI 2007
BibRef

Lu, J.H.[Jia-Hao], Sladoje, N.[Nataša], Stark, C.R.[Christina Runow], Ramqvist, E.D.[Eva Darai], Hirsch, J.M.[Jan-Michaél], Lindblad, J.[Joakim],
A Deep Learning Based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images,
ICIAR20(II:249-261).
Springer DOI 2007
BibRef

Takahama, S., Kurose, Y., Mukuta, Y., Abe, H., Fukayama, M., Yoshizawa, A., Kitagawa, M., Harada, T.,
Multi-Stage Pathological Image Classification Using Semantic Segmentation,
ICCV19(10701-10710)
IEEE DOI 2004
cancer, convolutional neural nets, feature extraction, gradient methods, image classification, image resolution BibRef

Roldán, N.[Nicolás], Rodriguez, L.[Lizeth], Hernandez, A.[Andrea], Cepeda, K.[Karen], Ondo-Méndez, A.[Alejandro], Suárez, S.L.C.[Sandra Liliana Cancino], Forero, M.G.[Manuel G.], Lopéz, J.M.[Juan M.],
A New Automatic Cancer Colony Forming Units Counting Method,
IbPRIA19(II:465-472).
Springer DOI 1910
BibRef

Wang, L.Y.[Li-Yang], Zhou, Y.[Yu], Matuszewski, B.J.[Bogdan J.],
A New Hybrid Method for Gland Segmentation in Histology Images,
CAIPWS19(17-27).
Springer DOI 1909
BibRef

Bergamini, L.[Luca], Trachtman, A.R.[Abigail Rose], Palazzi, A.[Andrea], del Negro, E.[Ercole], Dondona, A.C.[Andrea Capobianco], Marruchella, G.[Giuseppe], Calderara, S.[Simone],
Segmentation Guided Scoring of Pathological Lesions in Swine Through CNNs,
NTIAP19(352-360).
Springer DOI 1909
BibRef

Amiri, S.[Samya], Mahjoub, M.A.[Mohamed Ali],
HMDHBN: Hidden Markov Inducing a Dynamic Hierarchical Bayesian Network for Tumor Growth Prediction,
CAIP19(I:3-14).
Springer DOI 1909
BibRef

Lee, J.H.[Jae-Hyeok], Kim, S.T.[Seong Tae], Lee, H.[Hakmin], Ro, Y.M.[Yong Man],
Feature2Mass: Visual Feature Processing in Latent Space for Realistic Labeled Mass Generation,
BioIm18(VI:326-334).
Springer DOI 1905
BibRef

Dou, T., Zhou, W.,
2D and 3D Convolutional Neural Network Fusion for Predicting the Histological Grade of Hepatocellular Carcinoma,
ICPR18(3832-3837)
IEEE DOI 1812
Feature extraction, Matrix decomposition, Lesions, Correlation, Convolutional Neural Network BibRef

Licandro, R., Schlegl, T., Reiter, M., Diem, M., Dworzak, M., Schumich, A., Langs, G., Kampel, M.,
WGAN Latent Space Embeddings for Blast Identification in Childhood Acute Myeloid Leukaemia,
ICPR18(3868-3873)
IEEE DOI 1812
Blood, Cancer, Principal component analysis, Medical treatment, Cells (biology), Pediatrics BibRef

van Riel, S., van der Sommen, F., Zinger, S., Schoon, E.J., de With, P.H.N.,
Automatic Detection of Early Esophageal Cancer with CNNS Using Transfer Learning,
ICIP18(1383-1387)
IEEE DOI 1809
Cancer, Real-time systems, Support vector machines, Esophagus, Lesions, Training, transfer learning BibRef

Zhang, C., Song, Y., Zhang, D., Liu, S., Chen, M., Cai, W.,
Whole Slide Image Classification via Iterative Patch Labelling,
ICIP18(1408-1412)
IEEE DOI 1809
Training, Labeling, Feature extraction, Tumors, Cancer, Pathology, Pipelines, Iterative patch labelling, brain cancer, WSI, classification BibRef

Peng, B.B.[Bin-Bin], Chen, L.[Lin], Shang, M.S.[Ming-Sheng], Xu, J.J.[Jian-Jun],
Fully Convolutional Neural Networks for Tissue Histopathology Image Classification and Segmentation,
ICIP18(1403-1407)
IEEE DOI 1809
Image segmentation, Cancer, Feature extraction, Convolutional neural networks, Image classification, fully convolutional neural netw BibRef

Kalinovsky, A., Liauchuk, V., Tarasau, A.,
Lesion Detection in Ct Images Using Deep Learning Semantic Segmentation Technique,
PTVSBB17(13-17).
DOI Link 1805
BibRef

Jin, T., Cui, H., Zeng, S., Wang, X.,
Learning Deep Spatial Lung Features by 3D Convolutional Neural Network for Early Cancer Detection,
DICTA17(1-6)
IEEE DOI 1804
cancer, computerised tomography, feature extraction, feedforward neural nets, image classification, BibRef

Lian, C., Ruan, S., Denœux, T., Guo, Y., Vera, P.,
Accurate tumor segmentation in FDG-PET images with guidance of complementary CT images,
ICIP17(4447-4451)
IEEE DOI 1803
cancer, feature selection, image fusion, image segmentation, medical image processing, positron emission tomography, tumours, Unsupervised Learning BibRef

Zhang, Z., Xie, Y., Xing, F., McGough, M., Yang, L.,
MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network,
CVPR17(3549-3557)
IEEE DOI 1711
Bladder, Cancer, Computational modeling, Medical diagnostic imaging, Visualization BibRef

Wang, C., Bu, H., Bao, J., Li, C.,
A Level Set Method for Gland Segmentation,
Microscopy17(865-873)
IEEE DOI 1709
Glands, Image segmentation, Level set, Machine learning, Pathology, Shape, Standards BibRef

Li, C., Gupta, S., Rana, S., Nguyen, V.[Vu], Venkatesh, S., Ashley, D., Livingston, T.,
Multiple adverse effects prediction in longitudinal cancer treatment,
ICPR16(3156-3161)
IEEE DOI 1705
Cancer, Chemotherapy, Correlation, Fatigue, Optimization, Predictive models, Symmetric matrices, adverse effects, cancer treatment, longitudinal prediction, multiple-output, regression BibRef

Stanitsas, P., Cherian, A., Truskinovsky, A., Morellas, V., Papanikolopoulos, N.,
Active convolutional neural networks for cancerous tissue recognition,
ICIP17(1367-1371)
IEEE DOI 1803
Cancer, Data models, Entropy, Measurement uncertainty, Task analysis, Training, Uncertainty, active learning, cancer detection, uncertainty sampling BibRef

Stanitsas, P., Cherian, A., Li, X.[Xinyan], Truskinovsky, A., Morellas, V., Papanikolopoulos, N.,
Evaluation of feature descriptors for cancerous tissue recognition,
ICPR16(1490-1495)
IEEE DOI 1705
Cancer, Covariance matrices, Feature extraction, Geometry, Histograms, Image color analysis, Symmetric, matrices BibRef

Saha, B., Gupta, S., Phung, D., Venkatesh, S.,
Transfer learning for rare cancer problems via Discriminative Sparse Gaussian Graphical model,
ICPR16(537-542)
IEEE DOI 1705
Cancer, Cost function, Covariance matrices, Data models, Graphical models, Mathematical model, Training BibRef

Singh, V.R.,
Keynote speaker: Nano-cancer technology: New diagnostic and therapeutic devices,
IVPR17(1-1)
IEEE DOI 1704
Biographies;Ultrasonic variables measurement BibRef

Zhang, L.[Lei], Zhu, Y.[Ying],
CutPointVis: An Interactive Exploration Tool for Cancer Biomarker Cutpoint Optimization,
ISVC16(I: 55-64).
Springer DOI 1701
BibRef

Paul, A., Mukherjee, D.P.,
Gland segmentation from histology images using informative morphological scale space,
ICIP16(4121-4125)
IEEE DOI 1610
Cancer BibRef

Williams, E.,
The role of imaging in the detection, identification, and treatment of cancer,
AIPR15(1-6)
IEEE DOI 1605
biomedical imaging BibRef

Kourd Kaouther, E., Eddine Khelil, S., Hammoum, S.,
Study with RK4 ANOVA the location of the tumor at the smallest time for multi-images,
ICCVIA15(1-6)
IEEE DOI 1603
Gaussian distribution BibRef

Harai, Y., Tanaka, T.,
Automatic Diagnosis Support System Using Nuclear and Luminal Features,
DICTA15(1-8)
IEEE DOI 1603
cancer BibRef

Carneiro, G.[Gustavo], Peng, T.Y.[Ting-Ying], Bayer, C.[Christine], Navab, N.[Nassir],
Automatic detection of necrosis, normoxia and hypoxia in tumors from multimodal cytological images,
ICIP15(2429-2433)
IEEE DOI 1512
Classifier Combination BibRef

Sinha, D., Garain, U., Bandyopadhyay, S.,
Event extraction from cancer genetics literature,
ICAPR15(1-6)
IEEE DOI 1511
biology BibRef

Carneiro, G.[Gustavo], Peng, T.Y.[Ting-Ying], Bayer, C.[Christine], Navab, N.[Nassir],
Weakly-Supervised Structured Output Learning with Flexible and Latent Graphs Using High-Order Loss Functions,
ICCV15(648-656)
IEEE DOI 1602
BibRef
Earlier:
Flexible and Latent Structured Output Learning, Application to Histology,
MLMI15(220-228).
Springer DOI 1511
Tumors lack oxygen supply. BibRef

Liu, X.[Xiao], Shi, J.[Jun], Zhang, Q.[Qi],
Tumor Classification by Deep Polynomial Network and Multiple Kernel Learning on Small Ultrasound Image Dataset,
MLMI15(313-320).
Springer DOI 1511
BibRef

Lyksborg, M.[Mark], Puonti, O.[Oula], Agn, M.[Mikael], Larsen, R.[Rasmus],
An Ensemble of 2D Convolutional Neural Networks for Tumor Segmentation,
SCIA15(201-211).
Springer DOI 1506
BibRef

Albalooshi, F., Smith, S., Diskin, Y., Sidike, P., Asari, V.,
Automatic segmentation of carcinoma in radiographs,
AIPR14(1-6)
IEEE DOI 1504
biological tissues BibRef

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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications -- Thyroid .