Anant Madabhushi
Title
Cited by
Cited by
Year
Histopathological image analysis: A review
MN Gurcan, LE Boucheron, A Can, A Madabhushi, NM Rajpoot, B Yener
IEEE reviews in biomedical engineering 2, 147-171, 2009
12932009
Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases
A Janowczyk, A Madabhushi
Journal of pathology informatics 7, 2016
5262016
Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images
J Xu, L Xiang, Q Liu, H Gilmore, J Wu, J Tang, A Madabhushi
IEEE transactions on medical imaging 35 (1), 119-130, 2015
4732015
Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions
A Madabhushi, DN Metaxas
IEEE transactions on medical imaging 22 (2), 155-169, 2003
3402003
Image analysis and machine learning in digital pathology: Challenges and opportunities
A Madabhushi, G Lee
Medical image analysis 33, 170-175, 2016
3202016
Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology
S Naik, S Doyle, S Agner, A Madabhushi, M Feldman, J Tomaszewski
2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to …, 2008
3192008
Digital imaging in pathology: whole-slide imaging and beyond
F Ghaznavi, A Evans, A Madabhushi, M Feldman
Annual Review of Pathology: Mechanisms of Disease 8, 331-359, 2013
3052013
Assessment of algorithms for mitosis detection in breast cancer histopathology images
M Veta, PJ Van Diest, SM Willems, H Wang, A Madabhushi, A Cruz-Roa, ...
Medical image analysis 20 (1), 237-248, 2015
2912015
A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images
J Xu, X Luo, G Wang, H Gilmore, A Madabhushi
Neurocomputing 191, 214-223, 2016
2882016
Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge
G Litjens, R Toth, W van de Ven, C Hoeks, S Kerkstra, B van Ginneken, ...
Medical image analysis 18 (2), 359-373, 2014
2832014
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
2802014
A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection
AA Cruz-Roa, JEA Ovalle, A Madabhushi, FAG Osorio
International Conference on Medical Image Computing and Computer-Assisted …, 2013
2792013
Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features
S Doyle, S Agner, A Madabhushi, M Feldman, J Tomaszewski
2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to …, 2008
2762008
Automated grading of prostate cancer using architectural and textural image features
S Doyle, M Hwang, K Shah, A Madabhushi, M Feldman, J Tomaszeweski
2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to …, 2007
2762007
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
2502009
Identification of a microRNA panel for clear-cell kidney cancer
D Juan, G Alexe, T Antes, H Liu, A Madabhushi, C Delisi, S Ganesan, ...
Urology 75 (4), 835-841, 2010
2402010
A boosted Bayesian multiresolution classifier for prostate cancer detection from digitized needle biopsies
S Doyle, M Feldman, J Tomaszewski, A Madabhushi
IEEE transactions on biomedical engineering 59 (5), 1205-1218, 2010
2382010
Automated detection of prostatic adenocarcinoma from high-resolution ex vivo MRI
A Madabhushi, MD Feldman, DN Metaxas, J Tomaszeweski, D Chute
IEEE transactions on medical imaging 24 (12), 1611-1625, 2005
2122005
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
2072014
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
2002010
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