| Understanding neural networks through deep visualization J Yosinski, J Clune, A Nguyen, T Fuchs, H Lipson arXiv preprint arXiv:1506.06579, 2015 | 2476 | 2015 |
| Clinical-grade computational pathology using weakly supervised deep learning on whole slide images G Campanella, MG Hanna, L Geneslaw, A Miraflor, ... Nature medicine 25 (8), 1301-1309, 2019 | 2189 | 2019 |
| DeepPET: A deep encoder–decoder network for directly solving the PET image reconstruction inverse problem I Häggström, CR Schmidtlein, G Campanella, TJ Fuchs Medical image analysis 54, 253-262, 2019 | 331 | 2019 |
| Computational pathology: challenges and promises for tissue analysis TJ Fuchs, JM Buhmann Computerized Medical Imaging and Graphics 35 (7-8), 515-530, 2011 | 314 | 2011 |
| TAK1 suppresses a NEMO-dependent but NF-κB-independent pathway to liver cancer K Bettermann, M Vucur, J Haybaeck, C Koppe, J Janssen, F Heymann, ... Cancer cell 17 (5), 481-496, 2010 | 265 | 2010 |
| Hybrid deep learning on single wide-field optical coherence tomography scans accurately classifies glaucoma suspects H Muhammad, TJ Fuchs, N De Cuir, CG De Moraes, DM Blumberg, ... Journal of glaucoma 26 (12), 1086-1094, 2017 | 245 | 2017 |
| Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer I Cima, R Schiess, P Wild, M Kaelin, P Schüffler, V Lange, P Picotti, ... Proceedings of the National Academy of Sciences 108 (8), 3342-3347, 2011 | 228 | 2011 |
| Whole slide imaging equivalency and efficiency study: experience at a large academic center MG Hanna, VE Reuter, MR Hameed, LK Tan, S Chiang, C Sigel, ... Modern Pathology 32 (7), 916-928, 2019 | 190 | 2019 |
| Quickly boosting decision trees–pruning underachieving features early R Appel, T Fuchs, P Dollár, P Perona International conference on machine learning, 594-602, 2013 | 179 | 2013 |
| Robot-centric activity prediction from first-person videos: What will they do to me? MS Ryoo, TJ Fuchs, L Xia, JK Aggarwal, L Matthies Proceedings of the tenth annual ACM/IEEE international conference on human …, 2015 | 170 | 2015 |
| Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies P Raciti, J Sue, R Ceballos, R Godrich, JD Kunz, S Kapur, V Reuter, ... Modern Pathology 33 (10), 2058-2066, 2020 | 165 | 2020 |
| Risk-aware planetary rover operation: Autonomous terrain classification and path planning M Ono, TJ Fuchs, A Steffy, M Maimone, J Yen 2015 IEEE aerospace conference, 1-10, 2015 | 138 | 2015 |
| Prognostic relevance of Wnt-inhibitory factor-1 (WIF1) and Dickkopf-3 (DKK3) promoter methylation in human breast cancer J Veeck, PJ Wild, T Fuchs, PJ Schüffler, A Hartmann, R Knüchel, E Dahl BMC cancer 9, 1-13, 2009 | 136 | 2009 |
| H&E-stained whole slide image deep learning predicts SPOP mutation state in prostate cancer AJ Schaumberg, MA Rubin, TJ Fuchs BioRxiv, 064279, 2016 | 133 | 2016 |
| Neuron geometry extraction by perceptual grouping in ssTEM images V Kaynig, T Fuchs, JM Buhmann 2010 IEEE computer society conference on computer vision and pattern …, 2010 | 133 | 2010 |
| A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes SR Debats, D Luo, LD Estes, TJ Fuchs, KK Caylor Remote Sensing of Environment 179, 210-221, 2016 | 132 | 2016 |
| Machine learning approaches to analyze histological images of tissues from radical prostatectomies A Gertych, N Ing, Z Ma, TJ Fuchs, S Salman, S Mohanty, S Bhele, ... Computerized Medical Imaging and Graphics 46, 197-208, 2015 | 130 | 2015 |
| Independent real‐world application of a clinical‐grade automated prostate cancer detection system LM da Silva, EM Pereira, PGO Salles, R Godrich, R Ceballos, JD Kunz, ... The Journal of pathology 254 (2), 147-158, 2021 | 127 | 2021 |
| Deep multi-magnification networks for multi-class breast cancer image segmentation DJ Ho, DVK Yarlagadda, TM D’Alfonso, MG Hanna, A Grabenstetter, ... Computerized Medical Imaging and Graphics 88, 101866, 2021 | 118 | 2021 |
| The Bayesian group-lasso for analyzing contingency tables S Raman, TJ Fuchs, PJ Wild, E Dahl, V Roth Proceedings of the 26th Annual International Conference on Machine Learning …, 2009 | 118 | 2009 |
Gabriele CampanellaIcahn School of Medicine at Mount SinaiVerified email at mssm.edu