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A Self supervised learning framework for imbalanced medical imaging datasets

Yash Kumar Sharma, Charan Ramtej Kodi, Vineet Padmanabhan

Abstract

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.

A Self supervised learning framework for imbalanced medical imaging datasets

Abstract

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.

Paper Structure

This paper contains 10 sections, 3 equations, 6 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Experiments
  5. Dataset
  6. Dataset Analysis and Imbalance Characterization
  7. Implementation Details
  8. Representation Analysis
  9. Results
  10. Conclusions and Future Work

Figures (6)

  • Figure 2: Per-class accuracy of AMIMV-SSL on MedMNIST datasets.
  • Figure 3: t-SNE visualizations showing encoder representations that are learned by AMIMV-SSL across eleven MedMNIST datasets. Each subplot represents a distinct dataset, with colors representing ground truth class labels. The tight and distinctly separated clusters indicate strong and discriminative representation learning across various medical imaging modalities and imbalanced class distributions.
  • Figure 4: Confusion matrices of AMIMV-SSL across eleven MedMNIST datasets, including BloodMNIST, BreastMNIST, DermaMNIST, OCTMNIST, PathMNIST, PneumoniaMNIST, RetinaMNIST, TissueMNIST, OrganAMNIST, and OrganCMNIST
  • Figure : (a) AMIMV-SSL illustration
  • Figure : (a) AMIMV-SSL illustration
  • ...and 1 more figures