{"total":49,"rows":[{"id":"2107.02314","title":"The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification","created":"2021-07-05","cited_by_count":518,"area":null},{"id":"2210.04620","title":"FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings","created":"2022-10-10","cited_by_count":125,"area":null},{"id":"2110.01406","title":"MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation","created":"2021-09-29","cited_by_count":88,"area":null},{"id":"2105.06413","title":"OpenFL: An open-source framework for Federated Learning","created":"2021-05-13","cited_by_count":58,"area":null},{"id":"2112.10074","title":"QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results","created":"2021-12-19","cited_by_count":28,"area":null},{"id":"2207.06509","title":"One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis","created":"2022-07-13","cited_by_count":22,"area":null},{"id":"2403.05408","title":"FedFMS: Exploring Federated Foundation Models for Medical Image Segmentation","created":"2024-03-08","cited_by_count":21,"area":null},{"id":"2506.14318","title":"BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification","created":"2025-06-17","cited_by_count":15,"area":null},{"id":"2405.03636","title":"The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape","created":"2024-05-06","cited_by_count":13,"area":null},{"id":"2406.03323","title":"Comparative Benchmarking of Failure Detection Methods in Medical Image Segmentation: Unveiling the Role of Confidence Aggregation","created":"2024-06-05","cited_by_count":12,"area":null},{"id":"2111.08649","title":"FedCostWAvg: A new averaging for better Federated Learning","created":"2021-11-16","cited_by_count":11,"area":null},{"id":"2406.12815","title":"Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation","created":"2024-06-18","cited_by_count":9,"area":null},{"id":"2407.07557","title":"Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imaging","created":"2024-07-10","cited_by_count":8,"area":null},{"id":"2304.12117","title":"FedPIDAvg: A PID controller inspired aggregation method for Federated Learning","created":"2023-04-24","cited_by_count":7,"area":null},{"id":"2405.09409","title":"Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain","created":"2024-05-15","cited_by_count":5,"area":null},{"id":"2410.17265","title":"Federated brain tumor segmentation: an extensive benchmark","created":"2024-10-07","cited_by_count":5,"area":null},{"id":"2209.07704","title":"Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation","created":"2022-09-16","cited_by_count":4,"area":null},{"id":"2301.12617","title":"Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation","created":"2023-01-30","cited_by_count":4,"area":null},{"id":"2305.00738","title":"FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring","created":"2023-05-01","cited_by_count":4,"area":null},{"id":"2603.04887","title":"Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentation","created":"2026-03-05","cited_by_count":3,"area":null},{"id":"2304.09327","title":"Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging","created":"2023-04-18","cited_by_count":2,"area":null},{"id":"2310.15371","title":"Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation","created":"2023-10-23","cited_by_count":2,"area":null},{"id":"2409.16340","title":"Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review","created":"2024-09-24","cited_by_count":2,"area":null},{"id":"2412.20250","title":"Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation","created":"2024-12-28","cited_by_count":2,"area":null},{"id":"2412.20253","title":"Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation","created":"2024-12-28","cited_by_count":2,"area":null},{"id":"2202.08261","title":"Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation","created":"2022-02-16","cited_by_count":1,"area":null},{"id":"2212.08290","title":"Robust Learning Protocol for Federated Tumor Segmentation Challenge","created":"2022-12-16","cited_by_count":1,"area":null},{"id":"2301.00934","title":"Finding the Most Transferable Tasks for Brain Image Segmentation","created":"2023-01-03","cited_by_count":1,"area":null},{"id":"2310.11480","title":"Whole-brain radiomics for clustered federated personalization in brain tumor segmentation","created":"2023-10-17","cited_by_count":1,"area":null},{"id":"2411.02152","title":"FedPID: An Aggregation Method for Federated Learning","created":"2024-11-04","cited_by_count":1,"area":null},{"id":"2502.01000","title":"Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially","created":"2025-02-03","cited_by_count":1,"area":null},{"id":"2502.02340","title":"Transfer Risk Map: Mitigating Pixel-level Negative Transfer in Medical Segmentation","created":"2025-02-04","cited_by_count":1,"area":null},{"id":"2502.08829","title":"PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning","created":"2025-02-12","cited_by_count":1,"area":null},{"id":"2603.21660","title":"OmniFM: Toward Modality-Robust and Task-Agnostic Federated Learning for Heterogeneous Medical Imaging","created":"2026-03-23","cited_by_count":1,"area":null},{"id":"2203.05563","title":"Artificial Intelligence Solution for Effective Treatment Planning for Glioblastoma Patients","created":"2022-03-09","cited_by_count":0,"area":null},{"id":"2210.02733","title":"FedGraph: an Aggregation Method from Graph Perspective","created":"2022-10-06","cited_by_count":0,"area":null},{"id":"2407.12446","title":"Non-parametric regularization for class imbalance federated medical image classification","created":"2024-07-17","cited_by_count":0,"area":null},{"id":"2408.06183","title":"Centralized and Federated Heart Disease Classification Models Using UCI Dataset and their Shapley-value Based Interpretability","created":"2024-08-12","cited_by_count":0,"area":null},{"id":"2410.00173","title":"GaNDLF-Synth: A Framework to Democratize Generative AI for (Bio)Medical Imaging","created":"2024-09-30","cited_by_count":0,"area":null},{"id":"2410.06892","title":"Selecting the Best Sequential Transfer Path for Medical Image Segmentation with Limited Labeled Data","created":"2024-10-09","cited_by_count":0,"area":null},{"id":"2412.06494","title":"A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications","created":"2024-12-09","cited_by_count":0,"area":null},{"id":"2512.20610","title":"FedPOD: the deployable units of training for federated learning","created":"2025-12-23","cited_by_count":0,"area":null},{"id":"2602.20773","title":"Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalization","created":"2026-02-24","cited_by_count":0,"area":null},{"id":"2603.11659","title":"FL-MedSegBench: A Comprehensive Benchmark for Federated Learning on Medical Image Segmentation","created":"2026-03-12","cited_by_count":0,"area":null},{"id":"2605.06160","title":"Beyond Forgetting in Continual Medical Image Segmentation: A Comprehensive Benchmark Study","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.09025","title":"MedFL-Stress: A Systematic Robustness Evaluation of Federated Brain Tumor Segmentation under Cross-Hospital MRI Appearance Shift","created":"2026-05-09","cited_by_count":0,"area":null},{"id":"2608.19788","title":"MOSAIC: Modality-agnostic Spectral Alignment for Federated Image-level Weakly Supervised Tumor Segmentation under Client-specific Missing Modalities","created":"2026-08-20","cited_by_count":0,"area":null},{"id":"2609.19042","title":"FedASAP: Activation Statistics-driven Structured Adaptive Pruning for Efficient Personalized Federated Learning for Lesion Segmentation on brain MRI","created":"2026-07-20","cited_by_count":0,"area":null},{"id":"2610.04700","title":"Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation","created":"2026-10-03","cited_by_count":0,"area":null}],"next_cursor":null}