| A systematic classification of knowledge, reasoning, and context within the ARC dataset M Boratko, H Padigela, D Mikkilineni, P Yuvraj, R Das, A McCallum, ... arXiv preprint arXiv:1806.00358, 2018 | 59 | 2018 |
| Investigating the successes and failures of BERT for passage re-ranking H Padigela, H Zamani, WB Croft arXiv preprint arXiv:1905.01758, 2019 | 55 | 2019 |
| Additive MIL: Intrinsically interpretable multiple instance learning for pathology SA Javed, D Juyal, H Padigela, A Taylor-Weiner, L Yu, A Prakash Advances in Neural Information Processing Systems 35, 20689-20702, 2022 | 21 | 2022 |
| Additive mil: Intrinsic interpretability for pathology SA Javed, D Juyal, H Padigela, A Taylor-Weiner, L Yu, A Prakash arXiv preprint arXiv:2206.01794, 2022 | 6 | 2022 |
| An interface for annotating science questions M Boratko, H Padigela, D Mikkilineni, P Yuvraj, R Das, A McCallum, ... Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 6 | 2018 |
| Synthetic DOmain-Targeted Augmentation (S-DOTA) Improves Model Generalization in Digital Pathology SC Gullapally, Y Zhang, NK Mittal, D Kartik, S Srinivasan, K Rose, ... arXiv preprint arXiv:2305.02401, 2023 | 3 | 2023 |
| SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology D Juyal, S Shingi, SA Javed, H Padigela, C Shah, A Sampat, A Khosla, ... Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | 1 | 2024 |
| Abstract B010: Spatially-resolved prediction of gene expression signatures in H&E whole slide images using additive multiple instance learning models M Markey, J Kim, Z Goldstein, Y Gerardin, J Brosnan-Cashman, SA Javed, ... Molecular Cancer Therapeutics 22 (12_Supplement), B010-B010, 2023 | 1 | 2023 |
| Cell-type-specific nuclear morphology predicts genomic instability and prognosis in multiple cancer types J Abel, S Jain, D Rajan, H Padigela, K Leidal, A Prakash, J Conway, ... bioRxiv, 2023.05. 15.539600, 2023 | 1 | 2023 |
| Machine Learning-Based Prediction Of Geboes Score And Histologic Improvement And Remission Thresholds In Ulcerative Colitis Zahil Shanis, Harshith Padigela, Kathleen Sucipto, John Shamshoian, Jin Li ... Inflammatory Bowel Diseases 29 (Supplement_1), Pages S19–S20, 2023 | 1* | 2023 |
| AI-powered segmentation and analysis of nuclei morphology predicts genomic and clinical markers in multiple cancer types J Abel, S Jain, D Rajan, K Leidal, H Padigela, A Prakash, J Conway, ... Cancer Research 82 (12_Supplement), 464-464, 2022 | 1 | 2022 |
| Spatially-resolved prediction of gene expression signatures in H&E whole slide images using additive multiple instance learning models M Markey, J Kim, Z Goldstein, Y Gerardin, J Brosnan-Cashman, SA Javed, ... MOLECULAR CANCER THERAPEUTICS 22 (12), 2023 | | 2023 |
| Mo1754 MACHINE LEARNING-BASED PREDICTION OF GEBOES SCORE AND HISTOLOGIC IMPROVEMENT AND REMISSION THRESHOLDS IN ULCERATIVE COLITIS C Gaitán, Z Shanis, K Sucipto, J Shamshoian, J Li, G Hu, H Padigela, ... Gastroenterology 164 (6), S-894, 2023 | | 2023 |
| Synthetic DOmain-Targeted Augmentation (S-DOTA) Improves Model Generalization in Digital Pathology S Chowdary Gullapally, Y Zhang, NK Mittal, D Kartik, S Srinivasan, ... arXiv e-prints, arXiv: 2305.02401, 2023 | | 2023 |
| Abstract P4-09-08: AI-based quantitation of cancer cell and fibroblast nuclear morphology reflects transcriptomic heterogeneity and predicts survival in breast cancer J Abel, C Kirkup, F Kos, Y Gerardin, S Srinivasan, J Brosnan-Cashman, ... Cancer Research 83 (5_Supplement), P4-09-08-P4-09-08, 2023 | | 2023 |
| Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology S Ashar Javed, D Juyal, H Padigela, A Taylor-Weiner, L Yu, A Prakash arXiv e-prints, arXiv: 2206.01794, 2022 | | 2022 |
Amaro N Taylor-WeinerChief AI Officer, AbsciDirección de correo verificada de absci.com
