| 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 | 1880 | 2009 |
| 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 | 1096 | 2019 |
| 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 | 1010 | 2016 |
| 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 | 818 | 2015 |
| Image analysis and machine learning in digital pathology: Challenges and opportunities A Madabhushi, G Lee Medical image analysis 33, 170-175, 2016 | 686 | 2016 |
| 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 | 611 | 2019 |
| 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 | 564 | 2014 |
| 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 | 557 | 2014 |
| 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 | 454 | 2016 |
| 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 | 441 | 2013 |
| 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 | 438 | 2015 |
| 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 | 435 | 2018 |
| 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 | 427 | 2013 |
| 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 | 417 | 2017 |
| 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 | 417 | 2008 |
| 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 | 414 | 2017 |
| 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 | 386 | 2003 |
| 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 | 356 | 2018 |
| 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 | 351 | 2008 |
| 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 | 348 | 2014 |
Michael D FeldmanUniversity of PennsylavniaVerified email at mail.med.upenn.edu
