mlsanityData to 7 Oct 2026

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

arXiv 2303.05556 ↗First posted 9 Mar 2023cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Privacy and annotation bottlenecks are two major issues that profoundly affect the practicality of machine learning-based medical image analysis. Although significant progress has been made in these areas, these issues are not yet fully resolved. In this paper, we seek to tackle these concerns head-on and systematically explore the applicability of non-contrastive self-supervised learning (SSL) algorithms under federated learning (FL) simulations for medical image analysis. We conduct thorough experimentation of recently proposed state-of-the-art non-contrastive frameworks under standard FL setups. With the SoTA Contrastive Learning algorithm, SimCLR as our comparative baseline, we benchmark the performances of our 4 chosen non-contrastive algorithms under non-i.i.d. data conditions and with a varying number of clients. We present a holistic evaluation of these techniques on 6 standardized medical imaging datasets. We further analyse different trends inferred from the findings of our research, with the aim to find directions for further research based on ours. To the best of our knowledge, ours is the first to perform such a thorough analysis of federated self-supervised learning for medical imaging. All of our source code will be made public upon acceptance of the paper.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 41 references that are in the map. First posted on arXiv 9 Mar 2023.

Builds on

32 of 41 references.

References · 32

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  4. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  5. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  6. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  7. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  8. 2020Exploring Simple Siamese Representation Learningcited by 2,853
  9. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,818
  10. 2016Context Encoders: Feature Learning by Inpaintingcited by 1,424
  11. 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,379
  12. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
20 more
  1. 2016Colorful Image Colorizationcited by 1,113
  2. 2018Federated Learning with Non-IID Datacited by 1,099
  3. 2021VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learningcited by 1,013
  4. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 682
  5. 2019Self-supervised Visual Feature Learning with Deep Neural Networks: A Surveycited by 656
  6. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  7. 2020A Survey on Contrastive Self-supervised Learningcited by 553
  8. 2021Big Self-Supervised Models Advance Medical Image Classificationcited by 320
  9. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  10. 2021A Survey on Deep Semi-supervised Learningcited by 250
  11. 2020MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysiscited by 206
  12. 2021Decoupled Contrastive Learningcited by 141
  13. 2020Intriguing Properties of Contrastive Lossescited by 129
  14. 2021Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learningcited by 111
  15. 2021Self-supervised learning methods and applications in medical imaging analysis: A surveycited by 76
  16. 2022Understanding Collapse in Non-Contrastive Siamese Representation Learningcited by 42
  17. 2019Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Surveycited by 28
  18. 2022Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRIcited by 14
  19. 2022TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learningcited by 12
  20. 2022IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentationcited by 5

Built on by

Not cited yet.