20.7.1 Breast Cancer, Mammograms, Analysis, Mammography

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

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

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.
WWW Link. 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

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

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

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

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.[Xinbo], 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

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

Beck, A.H.[Andrew H.], Sangoi, A.R.[Ankur R.], Leung, S.[Samuel], Marinelli, R.J.[Robert J.], Nielsen, T.O.[Torsten O.], van de Vijver, M.J.[Marc J.], West, R.B.[Robert B.], van de Rijn, M.[Matt], Koller, D.[Daphne],
Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival,
Sci. Transl. Med.(3), Issue 108, 9 November 2011, pp. 108ra113
DOI Link BibRef 1111

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

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

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

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., Ji, Z., Ding, W., Lou, C., Yang, S., Xing, D.,
Ultrashort Microwave-Pumped Real-Time Thermoacoustic Breast Tumor Imaging System,
MedImg(35), No. 3, March 2016, pp. 839-844.
IEEE DOI 1603
Breast BibRef

McIntosh, C., Purdie, T.G.,
Contextual Atlas Regression Forests: Multiple-Atlas-Based Automated Dose Prediction in Radiation Therapy,
MedImg(35), No. 4, April 2016, pp. 1000-1012.
IEEE DOI 1604
Breast 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

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

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

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

Mehta, S., Mercan, E., Bartlett, J., Weaver, D., Elmore, J., Shapiro, L.,
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
BibRef

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
BibRef

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

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

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
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

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
BibRef

Galdran, A.[Adrian], Picón, A.[Artzai], Garrote, E.[Estibaliz], Pardo, D.[David],
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A gradient-like variational Bayesian approach: Application to microwave imaging for breast tumor detection,
ICIP14(1708-1712)
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A Novel Graph-Based Fisher Kernel Method for Semi-supervised Learning,
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A Novel Sphere-Based Maximum Margin Classification Method,
ICPR14(620-624)
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Mammary Gland Tumor Detection in Cats Using Ant Colony Optimisation,
ACPR13(942-945)
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Deshpande, D.S., Rajurkar, A.M., Manthalkar, R.M.,
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WSSIP15(113-116)
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Breast Parenchymal Pattern Analysis in Digital Mammography: Associations between Tabár and Birads Tissue Compositions,
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Meaningful Features for Computerized Detection of Breast Cancer,
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A Boosting-Based Approach to Refine the Segmentation of Masses in Mammography,
CIAP13(II:572-580).
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Development of electrochemical biosensor for breast cancer detection using gold nanoparticle doped CA 15-3 antibody and antigen interaction,
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ICIP12(2857-2860).
IEEE DOI 1302
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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).
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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).
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IMVIP12(113-116).
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Mass Detection in Digital Mammograms Using Optimized Gabor Filter Bank,
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Sardar, S.[Santu], Mishra, A.K.[Amit K.],
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ICIIP11(1-6).
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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).
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Classification of abnormalities in digitized mammograms using Extreme Learning Machine,
ICARCV10(2114-2117).
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Bag-of-Features Based Classification of Breast Parenchymal Tissue in the Mammogram via Jointly Selecting and Weighting Visual Words,
ICIG11(622-627).
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A Tree Classifier for Automatic Breast Tissue Classification Based on BIRADS Categories,
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A real time Breast Microwave Radar imaging reconstruction technique using simt based interpolation,
ICIP10(1389-1392).
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Mean shift based algorithm for mammographic breast mass detection,
ICIP10(3629-3632).
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False Positive Reduction in Breast Mass Detection Using Two-Dimensional PCA,
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ICIP10(4421-4424).
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Visual Perception Driven Registration of Mammograms,
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A mammography database and view system for the African American patients,
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ICIP01(II: 877-880).
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ICIP99(II:182-186).
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Detection of Spicules in Mammograms,
ICIP97(III: 520-523).
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And:
Automatic recognition of spicules in mammograms,
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Marroquin, E.M., Vos, C., Santamaria, E., Jove, X., Socoro, J.C.,
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ICIP96(II: 943-946).
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ICPR92(II:381-384).
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mammography application BibRef

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

Last update:Jun 14, 2018 at 16:13:32