| Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases A Janowczyk, A Madabhushi Journal of pathology informatics 7, 2016 | 610 | 2016 |
| SPIE Medical Imaging SYM Goh, A Irimia, PM Vespa, JD Van Horn, S Wang, M Fan, J Zhang, ... | 105 | 2010 |
| 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 | 98 | 2017 |
| Deep tissue photoacoustic imaging using a miniaturized 2-D capacitive micromachined ultrasonic transducer array SR Kothapalli, TJ Ma, S Vaithilingam, Ö Oralkan, BT Khuri-Yakub, ... IEEE Transactions on Biomedical Engineering 59 (5), 1199-1204, 2012 | 81 | 2012 |
| A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue JJ Nirschl, A Janowczyk, EG Peyster, R Frank, KB Margulies, ... PloS one 13 (4), e0192726, 2018 | 55 | 2018 |
| A high-throughput active contour scheme for segmentation of histopathological imagery J Xu, A Janowczyk, S Chandran, A Madabhushi Medical image analysis 15 (6), 851-862, 2011 | 50 | 2011 |
| Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images X Wang, A Janowczyk, Y Zhou, R Thawani, P Fu, K Schalper, V Velcheti, ... Scientific reports 7 (1), 1-10, 2017 | 47 | 2017 |
| Automated tubule nuclei quantification and correlation with oncotype DX risk categories in ER+ breast cancer whole slide images D Romo-Bucheli, A Janowczyk, H Gilmore, E Romero, A Madabhushi Scientific reports 6 (1), 1-9, 2016 | 47 | 2016 |
| A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images A Janowczyk, S Doyle, H Gilmore, A Madabhushi Computer Methods in Biomechanics and Biomedical Engineering: Imaging …, 2018 | 44 | 2018 |
| A weighted mean shift, normalized cuts initialized color gradient based geodesic active contour model: applications to histopathology image segmentation J Xu, A Janowczyk, S Chandran, A Madabhushi Medical Imaging 2010: Image Processing 7623, 76230Y, 2010 | 41 | 2010 |
| A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers D Romo‐Bucheli, A Janowczyk, H Gilmore, E Romero, A Madabhushi Cytometry Part A 91 (6), 566-573, 2017 | 37 | 2017 |
| HistoQC: an open-source quality control tool for digital pathology slides A Janowczyk, R Zuo, H Gilmore, M Feldman, A Madabhushi JCO clinical cancer informatics 3, 1-7, 2019 | 35 | 2019 |
| Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers C Lu, D Romo-Bucheli, X Wang, A Janowczyk, S Ganesan, H Gilmore, ... Laboratory investigation 98 (11), 1438-1448, 2018 | 35 | 2018 |
| High-throughput biomarker segmentation on ovarian cancer tissue microarrays via hierarchical normalized cuts A Janowczyk, S Chandran, R Singh, D Sasaroli, G Coukos, MD Feldman, ... IEEE transactions on biomedical engineering 59 (5), 1240-1252, 2011 | 33 | 2011 |
| Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer J Whitney, G Corredor, A Janowczyk, S Ganesan, S Doyle, ... BMC cancer 18 (1), 1-15, 2018 | 22 | 2018 |
| A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI K Ravichandran, N Braman, A Janowczyk, A Madabhushi Medical Imaging 2018: Computer-Aided Diagnosis 10575, 105750C, 2018 | 22 | 2018 |
| High-throughput prostate cancer gland detection, segmentation, and classification from digitized needle core biopsies J Xu, R Sparks, A Janowczyk, JE Tomaszewski, MD Feldman, ... International Workshop on Prostate Cancer Imaging, 77-88, 2010 | 21 | 2010 |
| An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival C Lu, JS Lewis, WD Dupont, WD Plummer, A Janowczyk, A Madabhushi Modern Pathology 30 (12), 1655-1665, 2017 | 19 | 2017 |
| Hierarchical normalized cuts: Unsupervised segmentation of vascular biomarkers from ovarian cancer tissue microarrays A Janowczyk, S Chandran, R Singh, D Sasaroli, G Coukos, MD Feldman, ... International Conference on Medical Image Computing and Computer-Assisted …, 2009 | 18 | 2009 |
| Deep learning tissue segmentation in cardiac histopathology images JJ Nirschl, A Janowczyk, EG Peyster, R Frank, KB Margulies, ... Deep learning for medical image analysis, 179-195, 2017 | 13 | 2017 |
Anant MadabhushiCase Western Reserve University, Department of Biomedical EngineeringVerified email at case.edu
