21.7.2 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Rahman, M.A.[Mohammad Akhlaqur], Jha, R.K.[Rajib Kumar], Gupta, A.K.[Abhishek Kumar],
Gabor phase response based scheme for accurate pectoral muscle boundary detection,
IET-IPR(13), No. 5, 18 April 2019, pp. 771-778.
DOI Link 1904
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

Yu, X.[Xiang], Zeng, N.Y.[Nian-Yin], Liu, S.[Shuai], Zhang, Y.D.[Yu-Dong],
Utilization of DenseNet201 for diagnosis of breast abnormality,
MVA(30), No. 7-8, October 2019, pp. 1135-1144.
WWW Link. 1911
BibRef

Wang, X.S.[Xu-Sheng], Chen, X.[Xing], Cao, C.J.[Cong-Jun],
Hierarchically engineering quality-related perceptual features for understanding breast cancer,
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,
IET-IPR(14), No. 3, 28 February 2020, pp. 495-505.
DOI Link 2002
BibRef

Shaikh, T.A.[Tawseef Ayoub], Ali, R.[Rashid], Beg, M.M.S.[M. M. Sufyan],
Transfer learning privileged information fuels CAD diagnosis of breast cancer,
MVA(31), No. 1, January 2020, pp. Article: 9
WWW Link. 2003
BibRef

Saha, M.[Monjoy], Arun, I.[Indu], Ahmed, R.[Rosina], Chatterjee, S.[Sanjoy], Chakraborty, C.[Chandan],
HscoreNet: A Deep network for estrogen and progesterone scoring using breast IHC images,
PR(102), 2020, pp. 107200.
Elsevier DOI 2003
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,
MedImg(39), No. 4, April 2020, pp. 934-943.
IEEE DOI 2004
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,
PRL(132), 2020, pp. 123-131.
Elsevier DOI 2005
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,
IJIST(30), No. 2, 2020, pp. 495-506.
DOI Link 2005
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,
IJIST(30), No. 2, 2020, pp. 391-400.
DOI Link 2005
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,
PRL(133), 2020, pp. 232-239.
Elsevier DOI 2005
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,
MedImg(39), No. 6, June 2020, pp. 2246-2255.
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,
MedImg(39), No. 6, June 2020, pp. 1930-1941.
IEEE DOI 2006
Histopathological image, reinforcement learning, breast cancer classification, deep learning BibRef

Sharma, S.[Shallu], Mehra, R.[Rajesh],
Effect of layer-wise fine-tuning in magnification-dependent classification of breast cancer histopathological image,
VC(36), No. 9, September 2020, pp. 1755-1769.
WWW Link. 2008
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Residual learning based CNN for breast cancer histopathological image classification,
IJIST(30), No. 3, 2020, pp. 621-635.
DOI Link 2008
breast cancer, convolutional neural network, data augmentation, deep features, histopathological image, residual learning BibRef

Yadavendra, Chand, S.[Satish],
A comparative study of breast cancer tumor classification by classical machine learning methods and deep learning method,
MVA(31), No. 6, August 2020, pp. Article46.
WWW Link. 2008
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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,
IJIST(30), No. 3, 2020, pp. 577-591.
DOI Link 2008
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,
IJIST(30), No. 3, 2020, pp. 513-526.
DOI Link 2008
breast cancer, collective animal behavior (CAB) algorithm, computer-aided diagnosis, discrete wavelet transform, Elman neural network BibRef

Ali, M.J.[Muhammad Junaid], Raza, B.[Basit], Shahid, A.R.[Ahmad Raza], Mahmood, F.[Fahad], Yousuf, M.A.[Muhammad Adil], Dar, A.H.[Amir Hanif], Iqbal, U.[Uzair],
Enhancing breast pectoral muscle segmentation performance by using skip connections in fully convolutional network,
IJIST(30), No. 4, 2020, pp. 1108-1118.
DOI Link 2011
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,
IET-IPR(14), No. 13, November 2020, pp. 3254-3259.
DOI Link 2012
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Procz, S., Roque, G., Avila, C., Racedo, J., Rueda, R., Santos, I., Fiederle, M.,
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

Shivhare, E.[Ekta], Saxena, V.N.[Vineeta Nigam],
Breast cancer diagnosis from mammographic images using optimized feature selection and neural network architecture,
IJIST(31), No. 1, 2021, pp. 253-269.
DOI Link 2102
breast cancer diagnosis, hybrid optimization, neural network, optimal feature selection, region growing segmentation BibRef

Silva, R.D.C.[Rodrigo Dalvit C.], Jenkyn, T.R.[Thomas R.],
Classification of Mammogram Abnormalities Using Legendre Moments,
IJIG(21), No. 1 2021, pp. 2150010.
DOI Link 2102
BibRef

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.
IEEE DOI 2102
Phantoms, X-ray imaging, Breast, Numerical models, Task analysis, Photonics, In silico imaging, mammography, phase contrast, x-ray BibRef

Jouirou, A.[Amira], Baâzaoui, A.[Abir], Barhoumi, W.[Walid],
Multi-view content-based mammogram retrieval using dynamic similarity and locality sensitive hashing,
PR(112), 2021, pp. 107786.
Elsevier DOI 2102
Multi-view information fusion, Multidimensional indexing, Locality sensitive hashing, Dynamic similarity BibRef

Fang, H.[Hong], Fan, H.Y.[Hong-Yu], Lin, S.[Shan], Qing, Z.[Zhang], Sheykhahmad, F.R.[Fatima Rashid],
Automatic breast cancer detection based on optimized neural network using whale optimization algorithm,
IJIST(31), No. 1, 2021, pp. 425-438.
DOI Link 2102
breast cancer, computer-aided diagnosis, image segmentation, neural networks, whale optimization algorithm BibRef

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

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

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

Sharma, S.[Shallu], Mehra, R.[Rajesh], Kumar, S.[Sumit],
Optimised CNN in conjunction with efficient pooling strategy for the multi-classification of breast cancer,
IET-IPR(15), No. 4, 2021, pp. 936-946.
DOI Link 2106
BibRef

Sowmyayani, S., Murugan, V.,
Multi-Type Classification Comparison of Mammogram Abnormalities,
IJIG(21), No. 3, July 2021, pp. 2150027.
DOI Link 2107
BibRef

Wang, C.[Churan], Li, J.[Jing], Zhang, F.[Fandong], Sun, X.W.[Xin-Wei], Dong, H.[Hao], Yu, Y.Z.[Yi-Zhou], Wang, Y.Z.[Yi-Zhou],
Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification,
IP(30), 2021, pp. 7980-7994.
IEEE DOI 2109
Lesions, Mammography, Data models, Generative adversarial networks, Estimation, Computer science, Breast cancer, Domain Knowledge, Mammogram Classification BibRef

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

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

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

Gupta, I.[Isha], Gupta, S.[Sheifali], Singh, S.[Swati],
Different CNN-based Architectures for Detection of Invasive Ductal Carcinoma in Breast Using Histopathology Images,
IJIG(21), No. 5 2021, pp. 2140003.
DOI Link 2201
BibRef

Sevim, G.[Gençay], Üncü, Y.A.[Yigit Ali], Mercan, T.[Tanju], Canpolat, M.[Murat],
Image reconstruction for diffuse optical tomography using bi-conjugate gradient and transpose-free quasi minimal residual algorithms and comparison of them,
IJIST(31), No. 4, 2021, pp. 1894-1905.
DOI Link 2112
bi-conjugate gradient, diffuse optical tomography, image reconstruction, reconstruction techniques, transpose free quasi minimal residual BibRef

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

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

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

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

Kashyap, R.[Ramgopal],
Dilated residual grooming kernel model for breast cancer detection,
PRL(159), 2022, pp. 157-164.
Elsevier DOI 2206
BibRef

Rajput, G.[Gunjan], Agrawal, S.[Shashank], Biyani, K.[Kunika], Vishvakarma, S.K.[Santosh Kumar],
Early breast cancer diagnosis using cogent activation function-based deep learning implementation on screened mammograms,
IJIST(32), No. 4, 2022, pp. 1101-1118.
DOI Link 2207
breast cancer classification, convolutional neural network, deep learning, detection, Mias dataset BibRef

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

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

Gargouri, N.[Norhène], Mokni, R.[Raouia], Damak, A.[Alima], Sellami, D.[Dorra], Abid, R.[Riadh],
An automatic breast computer-aided diagnosis scheme based on a weighted fusion of relevant features and a deep CNN classifier,
IET-IPR(16), No. 12, 2022, pp. 3394-3406.
DOI Link 2209
BibRef

Saadi, H.[Hayet], Merouani, H.F.[Hayet Farida], Melouah, A.[Ahlem], Guessoum, Z.[Zahia], Lemnadjlia, S.[Saida], Boukabach, N.[Nacereddine],
Multi-agents system for breast tumour detection in mammography by deep learning pre-processing and watershed segmentation,
IJCVR(12), No. 5, 2022, pp. 632-661.
DOI Link 2211
BibRef

Sharma, M.[Mukta], Mandloi, A.[Ayush], Bhattacharya, M.[Mahua],
A novel DeepML framework for multi-classification of breast cancer based on transfer learning,
IJIST(32), No. 6, 2022, pp. 1963-1977.
DOI Link 2212
biomedical application, breast cancer cells, deep learning, machine learning, multi-classification BibRef

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Shukla, P.K.[Praveen Kumar], Behera, A.R.[Aditya Ranjan],
A framework for breast cancer prediction and classification using deep learning,
IJCVR(14), No. 2, 2024, pp. 154-169.
DOI Link 2403
BibRef

Liang, Y.[Yinhao], Tang, W.J.[Wen-Jie], Wang, T.[Ting], Ng, W.W.Y.[Wing W. Y.], Chen, S.[Siyi], Jiang, K.[Kuiming], Wei, X.H.[Xin-Hua], Jiang, X.[Xinqing], Guo, Y.[Yuan],
HRadNet: A Hierarchical Radiomics-Based Network for Multicenter Breast Cancer Molecular Subtypes Prediction,
MedImg(43), No. 3, March 2024, pp. 1225-1236.
IEEE DOI 2403
Breast cancer, Biomedical imaging, Radiomics, Metadata, Training, Medical diagnostic imaging, Magnetic resonance imaging, multilayer features BibRef

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Nguyen, T.H.[Thanh-Huy], Kha, Q.H.[Quang Hien], Truong, T.N.T.[Thai Ngoc Toan], Lam, B.T.[Ba Thinh], Ngo, B.H.[Ba Hung], Dinh, Q.V.[Quang Vinh], Le, N.Q.K.[Nguyen Quoc Khanh],
Towards Robust Natural-Looking Mammography Lesion Synthesis on Ipsilateral Dual-Views Breast Cancer Analysis,
CVAMD23(2556-2565)
IEEE DOI 2401
BibRef

Moroz-Dubenco, C.[Cristiana], Diosan, L.[Laura], Andreica, A.[Anca],
Towards an Unsupervised Growcut Algorithm for Mammography Segmentation,
CVS23(102-111).
Springer DOI 2312
BibRef

Zhang, K.[Kunkun], Wang, B.[Bin],
Classification Task Assisted Segmentation Network for Breast Tumor Segmentation in Ultrasound Images,
ICIP23(3294-3298)
IEEE DOI 2312
BibRef

Oh, Y.T.[Young-Tack], Ko, E.[Eunsook], Park, H.[Hyunjin],
Semi-supervised Breast Lesion Segmentation Using Local Cross Triplet Loss for Ultrafast Dynamic Contrast-enhanced Mri,
ACCV22(VI:203-217).
Springer DOI 2307
BibRef

Godishala, A.K.[Aruna Kranthi], Yassin, H.[Hayati], Veena, R., Lai, D.T.C.[Daphne Teck Ching],
Breast Cancer Tumor Image Classification Using Deep Learning Image Data Generator,
ICIVC22(418-423)
IEEE DOI 2301
Deep learning, Support vector machines, Costs, Uncertainty, Detectors, Breast cancer, Generators, deep learning techniques, image detector BibRef

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

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

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

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

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

Silva, W.[Wilson], Carvalho, M.[Maria], Mavioso, C.[Carlos], Cardoso, M.J.[Maria J.], Cardoso, J.S.[Jaime S.],
Deep Aesthetic Assessment and Retrieval of Breast Cancer Treatment Outcomes,
IbPRIA22(108-118).
Springer DOI 2205
BibRef

Oliveira, H.P.[Helder P.], Cardoso, J.S.[Jaime S.], Magalhaes, A.[Andre], Cardoso, M.J.[Maria J.],
Simultaneous detection of prominent points on breast cancer conservative treatment images,
ICIP12(2841-2844).
IEEE DOI 1302
BibRef

Derbel, N.[Nouha], Tmar, H.[Hedi], Mahfoudhi, A.[Adel],
A Multi-View DCNN Based Method for Breast Cancer Screening,
DICTA21(1-6)
IEEE DOI 2201
Limiting, Databases, Digital images, Transfer learning, Neural networks, Predictive models, Delta-sigma modulation, DDSM BibRef

Villareal, R.J.T.[Rosiel Jazmine T.], Abu, P.A.R.[Patricia Angela R.],
Patch-Based Convolutional Neural Networks for TCGA-BRCA Breast Cancer Classification,
ISVC21(II:29-40).
Springer DOI 2112
BibRef

Rakhlin, A., Tiulpin, A., Shvets, A.A., Kalinin, A.A., Iglovikov, V.I., Nikolenko, S.,
Breast Tumor Cellularity Assessment Using Deep Neural Networks,
VRMI19(371-380)
IEEE DOI 2004
Image segmentation, Feature extraction, Decoding, Tumors, Breast cancer, Estimation, Deep Neural Networks, Cellularity, Diagnostic BibRef

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

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

Saini, M.[Manisha], Susan, S.[Seba],
Data Augmentation of Minority Class with Transfer Learning for Classification of Imbalanced Breast Cancer Dataset Using Inception-V3,
IbPRIA19(I:409-420).
Springer DOI 1910
BibRef

Oliveira, H.S.[Hugo S.], Teixeira, J.F.[João F.], Oliveira, H.P.[Hélder P.],
Lightweight Deep Learning Pipeline for Detection, Segmentation and Classification of Breast Cancer Anomalies,
CIAP19(II:707-715).
Springer DOI 1909
BibRef

Liu, X.F.[Xiao-Feng], Zou, Y.[Yang], Song, Y.H.[Yu-Hang], Yang, C.[Chao], You, J.[Jane], Kumar, B.V.K.V.[B. V. K. Vijaya],
Ordinal Regression with Neuron Stick-Breaking for Medical Diagnosis,
BioIm18(VI:335-344).
Springer DOI 1905
BibRef

Domingues, I., Abreu, P.H., Santos, J.,
Bi-Rads Classification of Breast Cancer: A New Pre-Processing Pipeline for Deep Models Training,
ICIP18(1378-1382)
IEEE DOI 1809
Training, Machine learning, Databases, Breast cancer, Mammography, Image pre-processing, Deep learning, BI-RADS classification BibRef

Weiss, N.[Nick], Kost, H.[Henning], Homeyer, A.[André],
Towards Interactive Breast Tumor Classification Using Transfer Learning,
ICIAR18(727-736).
Springer DOI 1807
BibRef

Liu, K.C.[Ke-Chun], Mokhtari, M.[Mojgan], Li, B.B.[Bei-Bin], Nofallah, S.[Shima], May, C.[Caitlin], Chang, O.[Oliver], Knezevich, S.[Stevan], Elmore, J.[Joann], Shapiro, L.G.[Linda G.],
Learning Melanocytic Proliferation Segmentation in Histopathology Images from Imperfect Annotations,
CVMI21(3761-3770)
IEEE DOI 2109
Image segmentation, Visualization, Annotations, Biopsy, Pipelines, Melanoma BibRef

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

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

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

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

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

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

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

Alcântara, R.[Rafaela], Junior, P.F.[Perfilino Ferreira], Ramos, A.[Aline],
Tsallis Entropy Extraction for Mammographic Region Classification,
CIARP16(451-458).
Springer DOI 1703
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

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

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],
Pectoral Muscle Segmentation in Mammograms Based on Cartoon-Texture Decomposition,
IbPRIA15(587-594).
Springer DOI 1506
BibRef

Oliver, A.[Arnau], Llado, X.[Xavier], Torrent, A.[Albert], Marti, J.[Joan],
One-shot segmentation of breast, pectoral muscle, and background in digitised mammograms,
ICIP14(912-916)
IEEE DOI 1502
Breast BibRef

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

Ayasso, H., Duchene, B., Mohammad-Djafari, A.,
A variational Bayesian approach for frequency diverse non-linear microwave imaging,
ICIP12(2069-2072).
IEEE DOI 1302
BibRef

Nguyen, P.[Phuoc], Tran, D.[Dat], Huang, X.[Xu], Ma, W.L.[Wan-Li],
A Novel Sphere-Based Maximum Margin Classification Method,
ICPR14(620-624)
IEEE DOI 1412
Breast cancer BibRef

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

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

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

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

Les, T.[Tomasz], Markiewicz, T.[Tomasz], Osowski, S.[Stanislaw], Cichowicz, M.[Marzena], Kozlowski, W.[Wojciech],
Automatic Evaluation System of FISH Images in Breast Cancer,
ICISP14(332-339).
Springer DOI 1406
BibRef

Chen, Z.L.[Zhi-Li], Wang, L.P.[Li-Ping], Denton, E.[Erika],
A Multiscale Blob Representation of Mammographic Parenchymal Patterns and Mammographic Risk Assessment,
CAIP13(II:346-353).
Springer DOI 1311
BibRef

Mourainst, D.C.[Daniel Cardoso], Lópezinst, M.A.G.[Miguel Angel Guevara], Cunhainst, P.[Pedro],
Benchmarking Datasets for Breast Cancer Computer-Aided Diagnosis (CADx),
CIARP13(I:326-333).
Springer DOI 1311
BibRef

He, W.[Wenda], Zwiggelaar, R.[Reyer],
Breast Parenchymal Pattern Analysis in Digital Mammography: Associations between Tabár and Birads Tissue Compositions,
CAIP13(II:386-393).
Springer DOI 1311
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Arias, J.A.[José Anibal], Rodríguez, V.[Verónica], Miranda, R.[Rosebet],
Meaningful Features for Computerized Detection of Breast Cancer,
CIARP13(II:198-205).
Springer DOI 1311
BibRef

Selwyna, P.G.C.[P. Georgia Chris], Loganathan, P.R.[Priyadarshini Ravandhu], Begam, K.H.[K. Haseena],
Development of electrochemical biosensor for breast cancer detection using gold nanoparticle doped CA 15-3 antibody and antigen interaction,
ICSIPR13(75-81).
IEEE DOI 1304
BibRef

Kim, D.H.[Dae Hoe], Choi, J.Y.[Jae Young], Ro, Y.M.[Yong Man],
Region based stellate features for classification of mammographic spiculated lesions in computer-aided detection,
ICIP12(2821-2824).
IEEE DOI 1302
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Kumar, M.S.[M. Sathish], Dinesh, E., Mohan Raj, T.,
Involuntary diagnosis of intraductal breast images using gaussian mixture model,
IMVIP12(113-116).
IEEE DOI 1302
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Krawczyk, B.[Bartosz], Jelen, l.[lukasz], Krzyzak, A.[Adam], Fevens, T.[Thomas],
Oversampling Methods for Classification of Imbalanced Breast Cancer Malignancy Data,
ICCVG12(483-490).
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Southwest12(1-4).
IEEE DOI 1205
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Sardar, S.[Santu], Mishra, A.K.[Amit K.],
An improved algorithm For UWB based imaging of breast tumors,
ICIIP11(1-6).
IEEE DOI 1112
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Abdaheer, M.S., Khan, E.[Ekram],
An automatic and simple breast tumor classification using area matching,
ICIIP11(1-5).
IEEE DOI 1112
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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).
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ICIG11(622-627).
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AIPR10(1-7).
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A real time Breast Microwave Radar imaging reconstruction technique using simt based interpolation,
ICIP10(1389-1392).
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ICIP10(4421-4424).
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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).
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CIAP05(900-906).
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ICIP01(II: 877-880).
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McGarry, G., Deriche, M.,
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ICIP00(Vol III: 416-419).
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ICIP00(Vol III: 432-435).
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ICIP97(III: 520-523).
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And:
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ICPR14(1185-1188)
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Breast BibRef

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

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