mlsanityData to 14 Oct 2026

A Self supervised learning framework for imbalanced medical imaging datasets

arXiv 2604.01947 ↗First posted 2 Apr 2026cs.CVcited by 1

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Abstract (arXiv)

Two problems often plague medical imaging analysis: 1) Non-availability of large quantities of labeled training data, and 2) Dealing with imbalanced data, i.e., abundant data are available for frequent classes, whereas data are highly limited for the rare class. Self supervised learning (SSL) methods have been proposed to deal with the first problem to a certain extent, but the issue of investigating the robustness of SSL to imbalanced data has rarely been addressed in the domain of medical image classification. In this work, we make the following contributions: 1) The MIMV method proposed by us in an earlier work is extended with a new augmentation strategy to construct asymmetric multi-image, multi-view (AMIMV) pairs to address both data scarcity and dataset imbalance in medical image classification. 2) We carry out a data analysis to evaluate the robustness of AMIMV under varying degrees of class imbalance in medical imaging . 3) We evaluate eight representative SSL methods in 11 medical imaging datasets (MedMNIST) under long-tailed distributions and limited supervision. Our experimental results on the MedMNIST dataset show an improvement of 4.25% on retinaMNIST, 1.88% on tissueMNIST, and 3.1% on DermaMNIST.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 29 of its 33 references that are in the map. First posted on arXiv 2 Apr 2026.

Builds on

29 of 33 references.

References · 29

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17 more
  1. 2021Big Self-Supervised Models Advance Medical Image Classificationcited by 320
  2. 2020Rethinking the Value of Labels for Improving Class-Imbalanced Learningcited by 216
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  17. 2025Maximally Useful and Minimally Redundant: The Key to Self Supervised Learning for Imbalanced Datacited by 1

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Citing papers per year: 2026: 1.

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