| Evaluation of climate-aware metrics tools for radiology informatics and artificial intelligence: toward a potential radiology ecolabel FX Doo, VS Parekh, A Kanhere, D Savani, AS Tejani, A Sapkota, HY Paul Journal of the American College of Radiology 21 (2), 239-247, 2024 | 3 | 2024 |
| Coarse race and ethnicity labels mask granular underdiagnosis disparities in deep learning models for chest radiograph diagnosis P Bachina, SP Garin, P Kulkarni, A Kanhere, J Sulam, VS Parekh, PH Yi Radiology 309 (2), e231693, 2023 | 2 | 2023 |
| Surgical aggregation: A federated learning framework for harmonizing distributed datasets with diverse tasks P Kulkarni, A Kanhere, HY Paul, VS Parekh arXiv preprint arXiv:2301.06683, 2023 | 2 | 2023 |
| SegViz: A federated learning framework for medical image segmentation from distributed datasets with different and incomplete annotations AU Kanhere, P Kulkarni, HY Paul, VS Parekh arXiv preprint arXiv:2301.07074, 2023 | 2 | 2023 |
| High-Throughput AI Inference for Medical Image Classification and Segmentation using Intelligent Streaming P Kulkarni, S Garin, A Kanhere, E Siegel, PH Yi, VS Parekh arXiv preprint arXiv:2305.15617, 2023 | 1 | 2023 |
| Text2Cohort: Democratizing the NCI Imaging Data Commons with Natural Language Cohort Discovery P Kulkarni, A Kanhere, PH Yi, VS Parekh arXiv preprint arXiv:2305.07637, 2023 | 1 | 2023 |
| Optimizing federated learning for medical image classification on distributed non-iid datasets with partial labels P Kulkarni, A Kanhere, PH Yi, VS Parekh arXiv preprint arXiv:2303.06180, 2023 | 1 | 2023 |
| From competition to collaboration: Making toy datasets on kaggle clinically useful for chest x-ray diagnosis using federated learning P Kulkarni, A Kanhere, PH Yi, VS Parekh arXiv preprint arXiv:2211.06212, 2022 | 1 | 2022 |
| Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning P Kulkarni, A Kanhere, H Kukreja, V Zhang, PH Yi, VS Parekh arXiv preprint arXiv:2404.07374, 2024 | | 2024 |
| Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment Anything Model for Crowd-Sourcing Medical Image Annotations P Kulkarni, A Kanhere, D Savani, A Chan, D Chatterjee, PH Yi, VS Parekh arXiv preprint arXiv:2403.15218, 2024 | | 2024 |
| Using Deep Learning to Predict Knee Osteoarthritis J Zhao, A Kanhere, P Kulkarni, D Chatterjee | | 2024 |
| One Copy Is All You Need: Resource-Efficient Streaming of Medical Imaging Data at Scale P Kulkarni, A Kanhere, E Siegel, PH Yi, VS Parekh arXiv preprint arXiv:2307.00438, 2023 | | 2023 |
| FEDERATED LEARNING BASED MEDICAL IMAGE SEGMENTATION FOR HETEROGENEOUS DATA SETS WITH PARTIAL ANNOTATIONS AU Kanhere Johns Hopkins University, 2023 | | 2023 |
| SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotations AU Kanhere, P Kulkarni, PH Yi, VS Parekh arXiv preprint arXiv:2301.07074, 2023 | | 2023 |
| Surgical Aggregation: Federated Class-Heterogeneous Learning P Kulkarni, A Kanhere, PH Yi, VS Parekh arXiv preprint arXiv:2301.06683, 2023 | | 2023 |
Paul H. YiUniversity of MarylandПотвърден имейл адрес: som.umaryland.edu
