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Anant Madabhushi
Anant Madabhushi
Department of Biomedical Engineering, Professor, Biomedical Engineering, Emory Univ & Georgia Tech
Verified email at emory.edu
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
18772009
Applications of machine learning in drug discovery and development
J Vamathevan, D Clark, P Czodrowski, I Dunham, E Ferran, G Lee, B Li, ...
Nature reviews Drug discovery 18 (6), 463-477, 2019
10892019
Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases
A Janowczyk, A Madabhushi
Journal of pathology informatics 7 (1), 29, 2016
10092016
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
8142015
Image analysis and machine learning in digital pathology: Challenges and opportunities
A Madabhushi, G Lee
Medical image analysis 33, 170-175, 2016
6872016
Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology
K Bera, KA Schalper, DL Rimm, V Velcheti, A Madabhushi
Nature reviews Clinical oncology 16 (11), 703-715, 2019
6092019
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
5642014
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
5572014
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
4532016
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
4412013
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
4382015
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS …
D Stoyanov, Z Taylor, G Carneiro, T Syeda-Mahmood, A Martel, ...
Springer, 2018
4342018
A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection
AA Cruz-Roa, JE Arevalo Ovalle, A Madabhushi, FA González Osorio
Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: 16th …, 2013
4242013
Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI
NM Braman, M Etesami, P Prasanna, C Dubchuk, H Gilmore, P Tiwari, ...
Breast Cancer Research 19 (1), 1-14, 2017
4172017
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
4172008
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 (1), 1-14, 2017
4142017
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
3862003
Radiomics and radiogenomics in lung cancer: a review for the clinician
R Thawani, M McLane, N Beig, S Ghose, P Prasanna, V Velcheti, ...
Lung cancer 115, 34-41, 2018
3552018
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
3512008
Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features
H Wang, A Cruz-Roa, A Basavanhally, H Gilmore, N Shih, M Feldman, ...
Journal of Medical Imaging 1 (3), 034003-034003, 2014
3482014
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