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Long-tailed multi-label retinal disease classification using alternate group training and gradient-based

Yingying Jian1, Xiaoyan Jia1, Han Zhang2

  • 1Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China.

Scientific Reports
|April 24, 2026
PubMed
Summary

This study introduces a new group training strategy to improve computer-aided diagnosis of ocular diseases. The method effectively handles imbalanced data and multiple co-occurring conditions, enhancing diagnostic accuracy.

Keywords:
Alternate group trainingKnowledge distillationLong-tailed classificationOcular disease recognitionRe-weighting

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Area of Science:

  • Ophthalmology
  • Computer Science
  • Artificial Intelligence

Background:

  • Ocular diseases are a leading cause of vision impairment, requiring accurate and timely diagnosis.
  • Current computer-aided diagnostic methods struggle with class imbalance and label co-occurrence in ocular disease data.
  • The long-tailed distribution of ocular diseases complicates accurate classification and diagnosis.

Purpose of the Study:

  • To propose a novel alternate group training strategy to address the multi-label long-tailed data distribution problem in ocular disease diagnosis.
  • To reduce the challenges posed by class imbalance and label co-occurrence in medical image analysis.
  • To enhance the performance of computer-aided diagnostic systems for ocular conditions.

Main Methods:

  • Data partitioning into semantic groups to mitigate class imbalance and label co-occurrence.
  • Alternate training of a teacher network using a gradient-based self-weighted loss.
  • Training a student model on the original dataset guided by the teacher network with a weighted class-balanced distillation loss.

Main Results:

  • The proposed alternate group training strategy significantly outperforms existing methods on a public ocular disease dataset.
  • The method effectively alleviates class-wise imbalance and instance-wise label co-occurrence.
  • Performance improvements were also observed when extending the single-teacher model to a multi-teacher framework.

Conclusions:

  • The novel alternate group training strategy is a highly effective approach for tackling multi-label, long-tailed data distribution problems in medical diagnostics.
  • This method offers a promising solution for improving the accuracy and robustness of computer-aided ocular disease detection.
  • The strategy's adaptability to multi-teacher models suggests broad applicability in complex diagnostic scenarios.