Distributed deep learning networks among institutions for medical imaging K Chang*, N Balachandar*, C Lam, D Yi, J Brown, A Beers, B Rosen, ... Journal of the American Medical Informatics Association 25 (8), 945-954, 2018 | 367 | 2018 |
Deep convolutional neural networks for lung cancer detection A Chon*, N Balachandar*, P Lu* Stanford University, 2017 | 103 | 2017 |
MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation YNT Vu*, R Wang*, N Balachandar*, C Liu, AY Ng, P Rajpurkar Machine Learning for Healthcare (MLHC) 5, 1-14, 2021 | 95 | 2021 |
Computer vision-based descriptive analytics of seniors’ daily activities for long-term health monitoring Z Luo, JT Hsieh, N Balachandar, S Yeung, G Pusiol, J Luxenberg, G Li, ... Machine Learning for Healthcare (MLHC) 2, 1-18, 2018 | 76 | 2018 |
Accounting for data variability in multi-institutional distributed deep learning for medical imaging N Balachandar, K Chang, J Kalpathy-Cramer, DL Rubin Journal of the American Medical Informatics Association 27 (5), 700-708, 2020 | 69 | 2020 |
Generalized zero-shot chest x-ray diagnosis through trait-guided multi-view semantic embedding with self-training A Paul, TC Shen, S Lee, N Balachandar, Y Peng, Z Lu, RM Summers IEEE Transactions on Medical Imaging 40 (10), 2642-2655, 2021 | 41 | 2021 |
Differentiation of active corneal infections from healed scars using deep learning M Tiwari, C Piech, M Baitemirova, NV Prajna, M Srinivasan, P Lalitha, ... Ophthalmology 129 (2), 139-146, 2022 | 36 | 2022 |
An experimental study of data heterogeneity in federated learning methods for medical imaging L Qu, N Balachandar, DL Rubin arXiv preprint arXiv:2107.08371, 2021 | 29 | 2021 |
Handling data heterogeneity with generative replay in collaborative learning for medical imaging L Qu, N Balachandar, M Zhang, D Rubin Medical image analysis 78, 102424, 2022 | 24 | 2022 |
Collaboration of AI Agents via Cooperative Multi-Agent Deep Reinforcement Learning N Balachandar, J Dieter, GS Ramachandran arXiv preprint arXiv:1907.00327, 2019 | 10 | 2019 |
Fitting and validation of an agent-based model for COVID-19 case forecasting in workplaces and universities V Kumaresan, N Balachandar, SF Poole, LJ Myers, P Varghese, ... Plos one 18 (3), e0283517, 2023 | 8 | 2023 |
Institutionally distributed deep learning networks K Chang*, N Balachandar*, CK Lam, D Yi, JM Brown, A Beers, BR Rosen, ... arXiv preprint arXiv:1709.05929, 2017 | 7 | 2017 |
Systems and Methods for Robust Federated Training of Neural Networks N Balachandar, DL Rubin, L Qu US Patent US-2021-0049473-A1, 2021 | 2 | 2021 |
Prediction of small molecule kinase inhibitors for chemotherapy using deep learning N Balachandar, C Liu, W Wang arXiv preprint arXiv:1907.00329, 2019 | 2 | 2019 |
Come-SEE: Cross-modality semantic embedding ensemble for generalized zero-shot diagnosis of chest radiographs A Paul, TC Shen, N Balachandar, Y Tang, Y Peng, Z Lu, RM Summers Interpretable and Annotation-Efficient Learning for Medical Image Computing …, 2020 | 1 | 2020 |
Optimizing Distributed Deep Learning Methods for Medical Image Data Heterogeneity Across Institutions N Balachandar, K Chang, J Kalpathy-Cramer, DL Rubin Radiological Society of North America 2019 Scientific Assembly and Annual …, 2019 | | 2019 |
Overcoming Data Variability Challenges to Federated Deep Learning for Medical Image Analysis N Balachandar, K Chang, J Kalpathy-Cramer, DL Rubin Conference on Machine Intelligence in Medical Imaging, 2019 | | 2019 |
Distributed deep learning networks among institutions for medical imaging A Beers, B Rosen, C Lam, DL Rubin, D Yi, J Kalpathy-Cramer, K Chang, ... University of Lincoln, 2018 | | 2018 |
Statistical Coupling Analysis of Intrinsically Disordered Proteins N Balachandar, G Alterovitz | | 2014 |
Differentiation of Active Corneal Infections from Healed Scars Using Deep Learning N Balachandar, JT Chua, T Redd, TM Lietman, S Thrun, CC Lin | | |