Federated Medical Image Segmentation

Federated medical image segmentation aims to collaboratively train accurate segmentation models across multiple medical institutions without directly sharing sensitive patient data. Current research focuses on addressing data heterogeneity, including variations in annotation quality and types of weak supervision (e.g., points, scribbles), by developing novel aggregation strategies and personalized model architectures. These advancements are crucial for improving the robustness and generalizability of medical image analysis while upholding patient privacy, ultimately leading to more accurate and equitable diagnostic tools.

Papers

July 2, 2024

FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completeness
Yangyang Xiang, Nannan Wu, Li Yu, Xin Yang, Kwang-Ting Cheng, Zengqiang Yan
Medical Image Segmentation Selective Annotation Medical Image Segmentation Datasets Federated Medical Image Segmentation

February 27, 2024

FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation
Li Lin, Yixiang Liu, Jiewei Wu, Pujin Cheng, Zhiyuan Cai, Kenneth K. Y. Wong, Xiaoying Tang
Weakly Supervised Learning Medical Image Segmentation Task Data Aggregation Sparse Annotation Multi Center Federated Medical Image Segmentation

December 20, 2023

FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise
Nannan Wu, Zhaobin Sun, Zengqiang Yan, Li Yu
Annotation Rather Data Aggregation Medical Image Segmentation Datasets Noisy Annotation Federated Medical Image Segmentation

October 23, 2023

Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation
Yongsong Huang, Wanqing Xie, Mingzhen Li, Mingmei Cheng, Jinzhou Wu, Weixiao Wang, Jane You, Xiaofeng Liu
Data Augmentation Federated Learning Feature Augmentation Cardiac Segmentation Federated Medical Image Segmentation

April 12, 2023

Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation
Li Lin, Jiewei Wu, Yixiang Liu, Kenneth K. Y. Wong, Xiaoying Tang
Segmentation Performance Weak Supervision Medical Image Segmentation Task Data Aggregation Anti Unification Adaptive Representation Federated Medical Image Segmentation Heterogeneous Label

March 29, 2023

Fair Federated Medical Image Segmentation via Client Contribution Estimation
Meirui Jiang, Holger R Roth, Wenqi Li, Dong Yang, Can Zhao, Vishwesh Nath, Daguang Xu, Qi Dou, Ziyue Xu
Client Contribution Proportional Fairness Federated Medical Image Segmentation Collaborative Fairness

March 9, 2023

An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis
Soumitri Chattopadhyay, Soham Ganguly, Sreejit Chaudhury, Sayan Nag, Samiran Chattopadhyay
Medical Image Analysis Medical Image Datasets Federated Self Supervised Learning Non Contrastive Self Supervised Learning Federated Medical Image Segmentation

July 11, 2022

Personalizing Federated Medical Image Segmentation via Local Calibration
Jiacheng Wang, Yueming Jin, Liansheng Wang
Medical Image Segmentation Task Personalized Model Consistent Prediction Federated Medical Image Segmentation Aware Calibration

April 16, 2022

IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation
Meirui Jiang, Hongzheng Yang, Chen Cheng, Qi Dou
Federated Learning Local Gradient Model Personalization Federated Medical Image Segmentation Global Gradient

March 18, 2022

Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation
An Xu, Wenqi Li, Pengfei Guo, Dong Yang, Holger Roth, Ali Hatamizadeh, Can Zhao, Daguang Xu, Heng Huang, Ziyue Xu
Deep Learning Medical Image Segmentation Medical Image Segmentation Task Generalization Gap Federated Medical Image Segmentation

Federated Medical Image Segmentation

Papers

FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completeness

FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation

FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise

Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation

Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation

Fair Federated Medical Image Segmentation via Client Contribution Estimation

An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis

Personalizing Federated Medical Image Segmentation via Local Calibration

IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation

Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation