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.
Elsevier DOI 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.
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

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

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

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

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

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

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, computer vision, 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
BibRef

Lin, H., Chen, H., Graham, S., Dou, Q., Rajpoot, N., Heng, P.,
Fast ScanNet: Fast and Dense Analysis of Multi-Gigapixel Whole-Slide Images for Cancer Metastasis Detection,
MedImg(38), No. 8, August 2019, pp. 1948-1958.
IEEE DOI 1908
Breast cancer, Image segmentation, Metastasis, Tumors, Task analysis, Image analysis, Histopathology image analysis, metastasis detection BibRef

Khan, S.[Sana_Ullah], Islam, N.[Naveed], Jan, Z.[Zahoor], Ud Din, I.[Ikram], Rodrigues, J.J.P.C.[Joel J. P. C],
A novel deep learning based framework for the detection and classification of breast cancer using transfer learning,
PRL(125), 2019, pp. 1-6.
Elsevier DOI 1909
Deep learning, Smart pattern recognition, Transfer learning, Breast cancer BibRef

Manessi, F.[Franco], Rozza, A.[Alessandro], Manzo, M.[Mario],
Dynamic graph convolutional networks,
PR(97), 2020, pp. 107000.
Elsevier DOI 1910
BibRef

Rozza, A.[Alessandro], Manzo, M.[Mario], Petrosino, A.[Alfredo],
A Novel Graph-Based Fisher Kernel Method for Semi-supervised Learning,
ICPR14(3786-3791)
IEEE DOI 1412
Breast cancer BibRef

Manzo, M.[Mario], Pellino, S.[Simone], Petrosino, A.[Alfredo], Rozza, A.[Alessandro],
A Novel Graph Embedding Framework for Object Recognition,
NORDIA14(341-352).
Springer DOI 1504
BibRef

Fang, Y.[Yan], Zhao, J.[Jing], Hu, L.[Lingzhi], Ying, X.P.[Xiao-Ping], Pan, Y.F.[Yan-Fang], Wang, X.P.[Xiao-Ping],
Image classification toward breast cancer using deeply-learned quality features,
JVCIR(64), 2019, pp. 102609.
Elsevier DOI 1911
Image classification, CNN, Quality score BibRef

Gu, S.H.[Sheng-Hua], Chen, Y.[Yi], Sheng, F.Q.[Fang-Qing], Zhan, T.M.[Tian-Ming], Chen, Y.J.[Yun-Jie],
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MVA(30), No. 7-8, October 2019, pp. 1111-1122.
Springer DOI 1911
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Yu, X.[Xiang], Zeng, N.Y.[Nian-Yin], Liu, S.[Shuai], Zhang, Y.D.[Yu-Dong],
Utilization of DenseNet201 for diagnosis of breast abnormality,
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Wang, X.S.[Xu-Sheng], Chen, X.[Xing], Cao, C.J.[Cong-Jun],
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JVCIR(64), 2019, pp. 102644.
Elsevier DOI 1911
Breast cancer, Deep learning, Quality-related, Weakly-supervised, Ranking algorithm BibRef

Qaiser, T., Rajpoot, N.M.,
Learning Where to See: A Novel Attention Model for Automated Immunohistochemical Scoring,
MedImg(38), No. 11, November 2019, pp. 2620-2631.
IEEE DOI 1911
Predictive models, Task analysis, Tumors, Computational modeling, Pathology, Breast, Training, Deep reinforcement learning, breast cancer BibRef

Eltrass, A.S.[Ahmed S.], Salama, M.S.[Mohamed S.],
Fully automated scheme for computer-aided detection and breast cancer diagnosis using digitised mammograms,
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Simultaneous Segmentation and Classification of Mass Region From Mammograms Using a Mixed-Supervision Guided Deep Model,
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IEEE DOI 2002
Mixed-supervision, deep learning, segmentation and classification, mammogram BibRef

Shaikh, T.A.[Tawseef Ayoub], Ali, R.[Rashid], Beg, M.M.S.[M. M. Sufyan],
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PR(102), 2020, pp. 107200.
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Breast, Estrogen, Progesterone, Encoder, Decoder BibRef

Oloumi, D., Winter, R.S.C., Kordzadeh, A., Boulanger, P., Rambabu, K.,
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,
MedImg(39), No. 4, April 2020, pp. 1184-1194.
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,
MedImg(39), No. 4, April 2020, pp. 1235-1244.
IEEE DOI 2004
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

Andreadis, T.[Theofilos], Emmanouilidis, C.[Christodoulos], Goumas, S.[Stefanos], Koulouriotis, D.[Dimitrios],
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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

Yadavendra, Chand, S.[Satish],
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Saxena, S.[Shweta], Shukla, S.[Sanyam], Gyanchandani, M.[Manasi],
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

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fully convolutional network, mammogram, mass segmentation, multichannel, multiscale 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,
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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,
MedImg(39), No. 12, December 2020, pp. 3766-3778.
IEEE DOI 2012
Detectors, II-VI semiconductor materials, Cadmium compounds, Photonics, Silicon, Phantoms, CdTe, X-ray attenuation efficiency BibRef

Cao, X., Chen, H., Li, Y., Peng, Y., Wang, S., Cheng, L.,
Uncertainty Aware Temporal-Ensembling Model for Semi-Supervised ABUS Mass Segmentation,
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IEEE DOI 2012
Uncertainty, Image segmentation, Breast, Reliability, Training, Biomedical imaging, breast mass 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,
MedImg(40), No. 2, February 2021, pp. 539-548.
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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],
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Multi-view information fusion, Multidimensional indexing, Locality sensitive hashing, Dynamic similarity BibRef

Fang, H.[Hong], Fan, H.[Hongyu], 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

Li, A.J.[Ai-Jing], Pan, Y.N.[Yu-Ning], Chen, B.[Bin], Huang, R.[Rong], Xia, J.[Jianbi], Jin, Y.H.[Yin-Hua], Zheng, J.J.[Jian-Jun],
Evaluation of quantitative dynamic contrast-enhanced (DCE)-MRI parameters using a reference region model in invasive ductal carcinoma (IDC) patients,
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breast invasive ductal carcinoma, DCE-MRI, prognostic factors BibRef

Gendin, D.I., Nayak, R., Wang, Y., Bayat, M., Fazzio, R.T., Oberai, A.A., Hall, T.J., Barbone, P.E., Alizad, A., Fatemi, M.,
Repeatability of Linear and Nonlinear Elastic Modulus Maps From Repeat Scans in the Breast,
MedImg(40), No. 2, February 2021, pp. 748-757.
IEEE DOI 2102
Strain, Elasticity, Imaging, Breast, Lesions, Ultrasonic imaging, Tracking, Elastography, breast cancer, inverse problem, quantitative, elastic nonlinearity 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,
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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,
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breast cancer, classification, crow-search algorithm and chaotic maps, mammogram images, wavelet BibRef

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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,
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Lightweight Deep Learning Pipeline for Detection, Segmentation and Classification of Breast Cancer Anomalies,
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Ordinal Regression with Neuron Stick-Breaking for Medical Diagnosis,
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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

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Towards Interactive Breast Tumor Classification Using Transfer Learning,
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Learning Melanocytic Proliferation Segmentation in Histopathology Images from Imperfect Annotations,
CVMI21(3761-3770)
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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)
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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)
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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)
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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)
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Breast cancer, Databases, Microscopy, Pathology, Training, Training, data BibRef

Alcântara, R.[Rafaela], Junior, P.F.[Perfilino Ferreira], Ramos, A.[Aline],
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Dhahbi, S.[Sami], Barhoumi, W.[Walid], Zagrouba, E.[Ezzeddine],
Content-Based Mammogram Retrieval Using Mixed Kernel PCA and Curvelet Transform,
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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

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).
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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
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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)
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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,
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ICIP14(912-916)
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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)
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A Novel Sphere-Based Maximum Margin Classification Method,
ICPR14(620-624)
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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)
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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)
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cancer BibRef

Mustra, M.[Mario], Peros, G., Zovko-Cihlar, B.,
Comparison of segmentation accuracy for different LUTs applied to digital mammograms,
WSSIP15(113-116)
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IMVIP12(113-116).
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ICCVG12(483-490).
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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).
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Lewis, S.H.[Samual H.], Dong, A.[Aijuan],
Detection of breast tumor candidates using marker-controlled watershed segmentation and morphological analysis,
Southwest12(1-4).
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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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ICIIP11(1-5).
IEEE DOI 1112
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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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Wang, J.Y.[Jing-Yan], Li, Y.P.[Yong-Ping], Zhang, Y.[Ying], Xie, H.[Honglan], Wang, C.[Chao],
Bag-of-Features Based Classification of Breast Parenchymal Tissue in the Mammogram via Jointly Selecting and Weighting Visual Words,
ICIG11(622-627).
IEEE DOI 1109
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Vállez, N.[Noelia], Bueno, G.[Gloria], Déniz-Suárez, O.[Oscar], Seone, J.A.[José A.], Dorado, J.[Julián], Pazos, A.[Alejandro],
A Tree Classifier for Automatic Breast Tissue Classification Based on BIRADS Categories,
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Yousef, W.A.[Waleed A.], Mustafa, W.A.[Waleed A.], Ali, A.A.[Ali A.], Abdelrazek, N.A.[Naglaa A.], Farrag, A.M.[Ahmed M.],
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AIPR10(1-7).
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Elshinawyz, M.Y.[Mona Y.], Badawyy, A.H.A.[Abdel-Hameed A.], Abdelmageedyy, W.W.[Wael W.], Chouikhaz, M.F.[Mohamed F.],
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AIPR10(1-7).
IEEE DOI 1010
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Flores-Tapia, D.[Daniel], Pistorius, S.[Stephen],
A real time Breast Microwave Radar imaging reconstruction technique using simt based interpolation,
ICIP10(1389-1392).
IEEE DOI 1009
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Sahba, F.[Farhang], Venetsanopoulos, A.[Anastasios],
Mean shift based algorithm for mammographic breast mass detection,
ICIP10(3629-3632).
IEEE DOI 1009
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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).
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Diez, Y.[Yago], Oliver, A.[Arnau], Llado, X.[Xavier], Marti, R.[Robert],
Comparison of registration methods using mamographic images,
ICIP10(4421-4424).
IEEE DOI 1009
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Harirchi, F.[Farshad], Radparvar, P.[Parham], Abrishami Moghaddam, H.[Hamid], Dehghan, F.[Faramarz], Giti, M.[Masoumeh],
Two-Level Algorithm for MCs Detection in Mammograms Using Diverse-Adaboost-SVM,
ICPR10(269-272).
IEEE DOI 1008
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Boucher, A., Cloppet, F., Vincent, N., Jouve, P.,
Visual Perception Driven Registration of Mammograms,
ICPR10(2374-2377).
IEEE DOI 1008
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An Exploration Scheme for Large Images: Application to Breast Cancer Grading,
ICPR10(3472-3475).
IEEE DOI 1008
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Cheikhouhou, I.[Imene], Djemal, K.[Khalifa], Maaref, H.[Hichem],
Mass Description for Breast Cancer Recognition,
ICISP10(576-584).
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Alolfe, M.A.[Mohammed A.], Mohamed, W.A.[Wael A.], Youssef, A.B.M.[Abou-Bakr M.], Mohamed, A.S.[Ahmed S.], Kadah, Y.M.[Yasser M.],
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ICIP09(2609-2612).
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AIPR05(152-157).
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A mammography database and view system for the African American patients,
AIPR04(139-144).
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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).
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Cheikhrouhou, I., Djemal, K., Sellami, D., Maaref, H., Derbel, N.,
New mass description in mammographies,
IPTA08(1-5).
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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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ACIVS08(xx-yy).
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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).
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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
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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).
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See also second-order analysis of stationary point processes, The. BibRef

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Classification of Breast Tissues in Mammogram Images Using Ripley's K Function and Support Vector Machine,
ICIAR07(899-910).
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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,
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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).
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Foggia, P.[Pasquale], Percannella, G.[Gennaro], Sansone, C.[Carlo], Vento, M.[Mario],
A Graph-Based Clustering Method and Its Applications,
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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,
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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).
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Roller, D.[Dieter], Lampasona, C.[Constanza],
A Method for Interpreting Pixel Grey Levels in Digital Mammography,
ICIAR06(II: 580-588).
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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).
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Ketsetzis, G.[Georgios], Brady, M.[Michael],
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MMBIA06(97).
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Lee, S.[Sarah], Stathaki, T.[Tania],
Mammogram Analysis Using Two-Dimensional Autoregressive Models: Sufficient or Not?,
CIAP05(900-906).
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Vitulano, S.[Sergio], Casanova, A.[Andrea],
The Role of Entropy: Mammogram Analysis,
ICIAR08(xx-yy).
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Bornefalk, H.[Hans],
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Mohammed, S., Yang, L.[Lei], Fiaidhi, J.,
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CRV04(172-179).
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Christoyianni, I., Dermatas, E., Kokkinakis, G.,
Automatic Detection of Abnormal Tissue in Mammography,
ICIP01(II: 877-880).
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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).
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A Model Based Approach to Object Detection in Digital Mammography,
ICIP99(II:182-186).
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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).
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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).
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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

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

Last update:Sep 19, 2021 at 21:11:01