mlsanityData to 14 Oct 2026

Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance

arXiv 2206.13079 ↗First posted 27 Jun 2022cs.CV cs.AIcited by 13

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Abstract (arXiv)

Despite recent progress on semi-supervised federated learning (FL) for medical image diagnosis, the problem of imbalanced class distributions among unlabeled clients is still unsolved for real-world use. In this paper, we study a practical yet challenging problem of class imbalanced semi-supervised FL (imFed-Semi), which allows all clients to have only unlabeled data while the server just has a small amount of labeled data. This imFed-Semi problem is addressed by a novel dynamic bank learning scheme, which improves client training by exploiting class proportion information. This scheme consists of two parts, i.e., the dynamic bank construction to distill various class proportions for each local client, and the sub-bank classification to impose the local model to learn different class proportions. We evaluate our approach on two public real-world medical datasets, including the intracranial hemorrhage diagnosis with 25,000 CT slices and skin lesion diagnosis with 10,015 dermoscopy images. The effectiveness of our method has been validated with significant performance improvements (7.61% and 4.69%) compared with the second-best on the accuracy, as well as comprehensive analytical studies. Code is available at https://github.com/med-air/imFedSemi.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 18 of its 30 references that are in the map. First posted on arXiv 27 Jun 2022.

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18 of 30 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Densely Connected Convolutional Networkscited by 6,720
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,085
  5. 2020Adaptive Federated Optimizationcited by 1,046
  6. 2021Model-Contrastive Federated Learningcited by 766
  7. 2020The Future of Digital Health with Federated Learningcited by 633
  8. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
  9. 2020Federated Learning with Matched Averagingcited by 564
  10. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 387
  11. 2020Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learningcited by 112
  12. 2020Federated Learning for Breast Density Classification: A Real-World Implementationcited by 62
6 more
  1. 2021Federated Semi-supervised Medical Image Classification via Inter-client Relation Matchingcited by 42
  2. 2021Federated Contrastive Learning for Decentralized Unlabeled Medical Imagescited by 34
  3. 2019Deep multi-class learning from label proportionscited by 33
  4. 2022Federated Contrastive Learning for Volumetric Medical Image Segmentationcited by 33
  5. 2020Semi-supervised Medical Image Classification with Global Latent Mixingcited by 13
  6. 2020Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Modelscited by 10

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Citing papers per year: 2022: 2; 2023: 5; 2024: 4; 2025: 2.

13 papers.

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  1. 2023FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneitycited by 33
  2. 2022FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classificationcited by 18
  3. 2023Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imagingcited by 16
  4. 2023FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolationcited by 14
  5. 2022Robust Split Federated Learning for U-shaped Medical Image Networkscited by 11
  6. 2024From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matchingcited by 5
  7. 2024Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learningcited by 5
  8. 2024FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneitycited by 5
  9. 2023FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoringcited by 4
  10. 2023Scale Federated Learning for Label Set Mismatch in Medical Image Classificationcited by 3
  11. 2025FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentationcited by 2
  12. 2024Non-parametric regularization for class imbalance federated medical image classificationcited by 0
1 more
  1. 2025Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matchingcited by 0