{"total":18,"rows":[{"id":"2204.10836","title":"Federated Learning Enables Big Data for Rare Cancer Boundary Detection","created":"2022-04-22","cited_by_count":84,"area":null},{"id":"2208.03392","title":"Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions","created":"2022-08-05","cited_by_count":35,"area":null},{"id":"2310.02874","title":"Recent Methodological Advances in Federated Learning for Healthcare","created":"2023-10-04","cited_by_count":11,"area":null},{"id":"2408.09227","title":"FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection","created":"2024-08-17","cited_by_count":8,"area":null},{"id":"2112.10001","title":"Cross-Domain Federated Learning in Medical Imaging","created":"2021-12-18","cited_by_count":6,"area":null},{"id":"2406.11636","title":"Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities","created":"2024-06-17","cited_by_count":5,"area":null},{"id":"2411.05824","title":"Navigating Distribution Shifts in Medical Image Analysis: A Survey","created":"2024-11-05","cited_by_count":5,"area":null},{"id":"2203.16622","title":"Federated Learning for the Classification of Tumor Infiltrating Lymphocytes","created":"2022-03-30","cited_by_count":4,"area":null},{"id":"2207.13502","title":"Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priors","created":"2022-07-27","cited_by_count":4,"area":null},{"id":"2304.05635","title":"Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation","created":"2023-04-12","cited_by_count":4,"area":null},{"id":"2205.01509","title":"MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning","created":"2022-05-03","cited_by_count":3,"area":null},{"id":"2408.03208","title":"Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery","created":"2024-08-06","cited_by_count":3,"area":null},{"id":"2504.20656","title":"Federated learning, ethics, and the double black box problem in medical AI","created":"2025-04-29","cited_by_count":1,"area":null},{"id":"2301.07074","title":"SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotations","created":"2023-01-17","cited_by_count":0,"area":null},{"id":"2302.06352","title":"Deep Anatomical Federated Network (Dafne): An open client-server framework for the continuous, collaborative improvement of deep learning-based medical image segmentation","created":"2023-02-13","cited_by_count":0,"area":null},{"id":"2308.02454","title":"SoK: Assessing the State of Applied Federated Machine Learning","created":"2023-08-03","cited_by_count":0,"area":null},{"id":"2310.09444","title":"Tackling Heterogeneity in Medical Federated learning via Vision Transformers","created":"2023-10-13","cited_by_count":0,"area":null},{"id":"2410.22223","title":"MAPUNetR: A Hybrid Vision Transformer and U-Net Architecture for Efficient and Interpretable Medical Image Segmentation","created":"2024-10-29","cited_by_count":0,"area":null}],"next_cursor":null}