mlsanityData to 7 Oct 2026

Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation

arXiv 1810.04304 ↗First posted 10 Oct 2018cs.LG stat.MLcited by 148

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Abstract (arXiv)

Deep learning models for semantic segmentation of images require large amounts of data. In the medical imaging domain, acquiring sufficient data is a significant challenge. Labeling medical image data requires expert knowledge. Collaboration between institutions could address this challenge, but sharing medical data to a centralized location faces various legal, privacy, technical, and data-ownership challenges, especially among international institutions. In this study, we introduce the first use of federated learning for multi-institutional collaboration, enabling deep learning modeling without sharing patient data. Our quantitative results demonstrate that the performance of federated semantic segmentation models (Dice=0.852) on multimodal brain scans is similar to that of models trained by sharing data (Dice=0.862). We compare federated learning with two alternative collaborative learning methods and find that they fail to match the performance of federated learning.

Key facts. Cited by 148 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Oct 2018.

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Citing papers per year: 2021: 42; 2022: 40; 2023: 18; 2024: 22; 2025: 18; 2026: 8.

148 papers.

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  1. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  2. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  3. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 121
  4. 2022Improving Generalization in Federated Learning by Seeking Flat Minimacited by 98
  5. 2021MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluationcited by 88
  6. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  7. 2021Deep Learning for Chest X-ray Analysis: A Surveycited by 84
  8. 2022Federated Learning Enables Big Data for Rare Cancer Boundary Detectioncited by 83
  9. 2023When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directionscited by 83
  10. 2021Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learningcited by 78
  11. 2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 78
  12. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
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  1. 2021Collaborative Unsupervised Visual Representation Learning from Decentralized Datacited by 68
  2. 2021HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Imagescited by 62
  3. 2021OpenFL: An open-source framework for Federated Learningcited by 58
  4. 2021The Federated Tumor Segmentation (FeTS) Challengecited by 49
  5. 2022Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillationcited by 45
  6. 2022Do Gradient Inversion Attacks Make Federated Learning Unsafe?cited by 44
  7. 2021Federated Semi-supervised Medical Image Classification via Inter-client Relation Matchingcited by 42
  8. 2022FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Drivingcited by 41
  9. 2022FedX: Unsupervised Federated Learning with Cross Knowledge Distillationcited by 40
  10. 2022Mechanisms that Incentivize Data Sharing in Federated Learningcited by 38
  11. 2022Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learningcited by 35
  12. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
  13. 2022Federated Learning of Generative Image Priors for MRI Reconstructioncited by 33
  14. 2022Federated Contrastive Learning for Volumetric Medical Image Segmentationcited by 32
  15. 2021A Systematic Collection of Medical Image Datasets for Deep Learningcited by 29
  16. 2022Personalizing Federated Medical Image Segmentation via Local Calibrationcited by 28
  17. 2022CrowdGuard: Federated Backdoor Detection in Federated Learningcited by 26
  18. 2021GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imagingcited by 25
  19. 2023MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labelingcited by 25
  20. 2021RoFL: Robustness of Secure Federated Learningcited by 23
  21. 2023BayBFed: Bayesian Backdoor Defense for Federated Learningcited by 23
  22. 2022FedMix: Mixed Supervised Federated Learning for Medical Image Segmentationcited by 22
  23. 2022One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesiscited by 22
  24. 2023Federated Learning for Computer Visioncited by 22
  25. 2024Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchorscited by 21
  26. 2021EasyFL: A Low-code Federated Learning Platform For Dummiescited by 20
  27. 2021Memory-aware curriculum federated learning for breast cancer classificationcited by 20
  28. 2021FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentationcited by 19
  29. 2021Membership Inference Attacks on Deep Regression Models for Neuroimagingcited by 19
  30. 2021SplitAVG: A heterogeneity-aware federated deep learning method for medical imagingcited by 19
  31. 2021Multi-task Federated Learning for Heterogeneous Pancreas Segmentationcited by 19
  32. 2023Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognitioncited by 19
  33. 2021Dopamine: Differentially Private Federated Learning on Medical Datacited by 18
  34. 2022Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phasescited by 18
  35. 2021Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Trainingcited by 17
  36. 2023Transformers in Healthcare: A Surveycited by 17
  37. 2021Scalable federated machine learning with FEDncited by 16
  38. 2022Distributed Contrastive Learning for Medical Image Segmentationcited by 16

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