|Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks|
A Cruz-Roa, A Basavanhally, F González, H Gilmore, M Feldman, ...
Medical Imaging 2014: Digital Pathology 9041, 904103, 2014
|Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology|
AN Basavanhally, S Ganesan, S Agner, JP Monaco, MD Feldman, ...
IEEE Transactions on biomedical engineering 57 (3), 642-653, 2009
|Expectation–maximization-driven geodesic active contour with overlap resolution (emagacor): Application to lymphocyte segmentation on breast cancer histopathology|
H Fatakdawala, J Xu, A Basavanhally, G Bhanot, S Ganesan, M Feldman, ...
IEEE Transactions on Biomedical Engineering 57 (7), 1676-1689, 2010
|Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features|
H Wang, AC Roa, AN Basavanhally, HL Gilmore, N Shih, M Feldman, ...
Journal of Medical Imaging 1 (3), 034003, 2014
|Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent|
A Cruz-Roa, H Gilmore, A Basavanhally, M Feldman, S Ganesan, ...
Scientific reports 7, 46450, 2017
|Computer-aided prognosis: predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data|
A Madabhushi, S Agner, A Basavanhally, S Doyle, G Lee
Computerized medical imaging and graphics 35 (7-8), 506-514, 2011
|In vivo gated 4D imaging of the embryonic heart using optical coherence tomography|
MW Jenkins, OQ Chughtai, AN Basavanhally, M Watanabe, AM Rollins
Journal of biomedical optics 12 (3), 030505, 2007
|Multi-field-of-view framework for distinguishing tumor grade in ER+ breast cancer from entire histopathology slides|
A Basavanhally, S Ganesan, M Feldman, N Shih, C Mies, J Tomaszewski, ...
IEEE transactions on biomedical engineering 60 (8), 2089-2099, 2013
|Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection|
H Wang, A Cruz-Roa, A Basavanhally, H Gilmore, N Shih, M Feldman, ...
Medical Imaging 2014: Digital Pathology 9041, 90410B, 2014
|Stain normalization using sparse autoencoders (StaNoSA): application to digital pathology|
A Janowczyk, A Basavanhally, A Madabhushi
Computerized Medical Imaging and Graphics 57, 50-61, 2017
|Incorporating domain knowledge for tubule detection in breast histopathology using O'Callaghan neighborhoods|
A Basavanhally, E Yu, J Xu, S Ganesan, M Feldman, J Tomaszewski, ...
Medical Imaging 2011: Computer-Aided Diagnosis 7963, 796310, 2011
|Multi-field-of-view strategy for image-based outcome prediction of multi-parametric estrogen receptor-positive breast cancer histopathology: Comparison to Oncotype DX|
A Basavanhally, M Feldman, N Shih, C Mies, J Tomaszewski, S Ganesan, ...
Journal of pathology informatics 2, 2011
|Towards improved cancer diagnosis and prognosis using analysis of gene expression data and computer aided imaging|
G Alexe, J Monaco, S Doyle, A Basavanhally, A Reddy, M Seiler, ...
Experimental Biology and Medicine 234 (8), 860-879, 2009
|Integrated diagnostics: a conceptual framework with examples|
A Madabhushi, S Doyle, G Lee, A Basavanhally, J Monaco, S Masters, ...
Clinical Chemistry and Laboratory Medicine (CCLM) 48 (7), 989-998, 2010
|EM-based segmentation-driven color standardization of digitized histopathology|
A Basavanhally, A Madabhushi
Medical Imaging 2013: Digital Pathology 8676, 86760G, 2013
|Estimation of smooth growth trajectories with controlled acceleration from time series shape data|
J Fishbaugh, S Durrleman, G Gerig
International Conference on Medical Image Computing and Computer-Assisted …, 2011
|Computer-aided prognosis of ER+ breast cancer histopathology and correlating survival outcome with oncotype DX assay|
A Basavanhally, J Xu, A Madabhushi, S Ganesan
2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro …, 2009
|System and method for accurate and rapid identification of diseased regions on biological images with applications to disease diagnosis and prognosis|
A Madabhushi, J Monaco, J Tomaszewski, M Feldman, A Basavanhally
US Patent 8,718,340, 2014
|High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection|
A Cruz-Roa, H Gilmore, A Basavanhally, M Feldman, S Ganesan, N Shih, ...
PloS one 13 (5), e0196828, 2018
|A boosted distance metric: application to content based image retrieval and classification of digitized histopathology|
J Naik, S Doyle, A Basavanhally, S Ganesan, MD Feldman, ...
Medical Imaging 2009: Computer-Aided Diagnosis 7260, 72603F, 2009