# Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching

arXiv: https://arxiv.org/abs/2106.08600 · HTML: https://mlsanity.com/paper/2106.08600/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 35 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 24 of 24)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://mlsanity.com/paper/1703.01780/) (2017)
- [Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/1907.07034/) (2019)
- [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://mlsanity.com/paper/2103.06030/) (2021)
- [Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis](https://mlsanity.com/paper/1804.06353/) (2018)
- [Privacy-preserving Federated Brain Tumour Segmentation](https://mlsanity.com/paper/1910.00962/) (2019)
- [Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation](https://mlsanity.com/paper/1810.04304/) (2018)
- [Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results](https://mlsanity.com/paper/2001.05647/) (2020)
- [MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data](https://mlsanity.com/paper/2002.03366/) (2020)
- [Deep Learning for Medical Image Processing: Overview, Challenges and Future](https://mlsanity.com/paper/1704.06825/) (2017)
- [Unpaired Multi-modal Segmentation via Knowledge Distillation](https://mlsanity.com/paper/2001.03111/) (2020)
- [Federated Learning for Breast Density Classification: A Real-World Implementation](https://mlsanity.com/paper/2009.01871/) (2020)
- [Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model](https://mlsanity.com/paper/2005.07377/) (2020)
- [Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model](https://mlsanity.com/paper/1903.01248/) (2019)
- [FocalMix: Semi-Supervised Learning for 3D Medical Image Detection](https://mlsanity.com/paper/2003.09108/) (2020)
- [Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data](https://mlsanity.com/paper/1810.08553/) (2018)
- [Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data](https://mlsanity.com/paper/2006.00080/) (2020)
- [GraphXNET- Chest X-Ray Classification Under Extreme Minimal Supervision](https://mlsanity.com/paper/1907.10085/) (2019)
- [Fed-Sim: Federated Simulation for Medical Imaging](https://mlsanity.com/paper/2009.00668/) (2020)
- [Semi-supervised Medical Image Classification with Global Latent Mixing](https://mlsanity.com/paper/2005.11217/) (2020)
- [Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models](https://mlsanity.com/paper/2008.11364/) (2020)

## Built on by (most cited first, 30 of 42)

- [Federated Learning for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2306.05980/) (2023)
- [Specificity-Preserving Federated Learning for MR Image Reconstruction](https://mlsanity.com/paper/2112.05752/) (2021)
- [FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity](https://mlsanity.com/paper/2305.05230/) (2023)
- [RSCFed: Random Sampling Consensus Federated Semi-supervised Learning](https://mlsanity.com/paper/2203.13993/) (2022)
- [FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation](https://mlsanity.com/paper/2205.01840/) (2022)
- [IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation](https://mlsanity.com/paper/2204.08467/) (2022)
- [Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases](https://mlsanity.com/paper/2203.07345/) (2022)
- [FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification](https://mlsanity.com/paper/2206.13803/) (2022)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance](https://mlsanity.com/paper/2206.13079/) (2022)
- [Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators](https://mlsanity.com/paper/2307.05358/) (2023)
- [Enable the Right to be Forgotten with Federated Client Unlearning in Medical Imaging](https://mlsanity.com/paper/2407.02356/) (2024)
- [A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications](https://mlsanity.com/paper/2403.01387/) (2024)
- [Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation](https://mlsanity.com/paper/2207.11191/) (2022)
- [Towards Unbiased Training in Federated Open-world Semi-supervised Learning](https://mlsanity.com/paper/2305.00771/) (2023)
- [Robust Training of Federated Models with Extremely Label Deficiency](https://mlsanity.com/paper/2402.14430/) (2024)
- [FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation](https://mlsanity.com/paper/2402.17502/) (2024)
- [Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction](https://mlsanity.com/paper/2308.10910/) (2023)
- [FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity](https://mlsanity.com/paper/2406.18995/) (2024)
- [Navigating Distribution Shifts in Medical Image Analysis: A Survey](https://mlsanity.com/paper/2411.05824/) (2024)
- [Invariant Shape Representation Learning For Image Classification](https://mlsanity.com/paper/2411.12201/) (2024)
- [FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring](https://mlsanity.com/paper/2305.00738/) (2023)
- [Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification](https://mlsanity.com/paper/2307.14959/) (2023)
- [Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data](https://mlsanity.com/paper/2310.18815/) (2023)
- [Dual Class-Aware Contrastive Federated Semi-Supervised Learning](https://mlsanity.com/paper/2211.08914/) (2022)
- [Scale Federated Learning for Label Set Mismatch in Medical Image Classification](https://mlsanity.com/paper/2304.06931/) (2023)
- [Multi-source-free Domain Adaptation via Uncertainty-aware Adaptive Distillation](https://mlsanity.com/paper/2402.06213/) (2024)
- [Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency](https://mlsanity.com/paper/2403.12695/) (2024)
- [Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading](https://mlsanity.com/paper/2302.06089/) (2023)
- [A New Perspective to Boost Performance Fairness for Medical Federated Learning](https://mlsanity.com/paper/2410.19765/) (2024)

## Abstract (arXiv)

Federated learning (FL) has emerged with increasing popularity to collaborate distributed medical institutions for training deep networks. However, despite existing FL algorithms only allow the supervised training setting, most hospitals in realistic usually cannot afford the intricate data labeling due to absence of budget or expertise. This paper studies a practical yet challenging FL problem, named textitFederated Semi-supervised Learning (FSSL), which aims to learn a federated model by jointly utilizing the data from both labeled and unlabeled clients (i.e., hospitals). We present a novel approach for this problem, which improves over traditional consistency regularization mechanism with a new inter-client relation matching scheme. The proposed learning scheme explicitly connects the learning across labeled and unlabeled clients by aligning their extracted disease relationships, thereby mitigating the deficiency of task knowledge at unlabeled clients and promoting discriminative information from unlabeled samples. We validate our method on two large-scale medical image classification datasets. The effectiveness of our method has been demonstrated with the clear improvements over state-of-the-arts as well as the thorough ablation analysis on both tasksfootnoteCode will be made available at urlhttps://github.com/liuquande/FedIRM.