Yash Kumar Sharma | Searcharxiv

arXiv2026

A Self supervised learning framework for imbalanced medical imaging datasets

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.

cs.CV↗

arXiv2025

Maximally Useful and Minimally Redundant: The Key to Self Supervised Learning for Imbalanced Data

Contrastive self supervised learning(CSSL) usually makes use of the multi-view assumption which states that all relevant information must be shared between all views. The main objective of CSSL is to maximize the mutual information(MI) between representations of different views and at the same time compress irrelevant information in each representation. Recently, as part of future work, Schwartz Ziv & Yan LeCun pointed out that, when the multi-view assumption is violated, one of the most significant challenges in SSL is in identifying new methods to separate relevant from irrelevant information based on alternative assumptions. Taking a cue from this intuition we make the following contributions in this paper: 1) We develop a CSSL framework wherein multiple images and multiple views(MIMV) are considered as input, which is different from the traditional multi-view assumption 2) We adopt a novel augmentation strategy that includes both normalized (invertible) and augmented (non-invertible) views so that complete information of one image can be preserved and hard augmentation can be chosen for the other image 3) An Information bottleneck(IB) principle is outlined for MIMV to produce optimal representations 4) We introduce a loss function that helps to learn better representations by filtering out extreme features 5) The robustness of our proposed framework is established by applying it to the imbalanced dataset problem wherein we achieve a new state-of-the-art accuracy (2% improvement in Cifar10-LT using Resnet-18, 5% improvement in Cifar100-LT using Resnet-18 and 3% improvement in Imagenet-LT (1k) using Resnet-50).

cs.CV↗