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Dual-task collaborative optimization for fundus image disease diagnosis and quality assessment.

Hao Liu1, Kanwei Wang1, Yuexin Luo1

  • 1School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, Jiangsu, People's Republic of China.

Medical Engineering & Physics
|February 5, 2026
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Summary

This study introduces a novel network (DTCONet) that jointly optimizes fundus image quality assessment and disease diagnosis. The dual-task approach enhances diagnostic accuracy by leveraging the relationship between image quality and pathological features.

Keywords:
disease diagnosisdual-task collaborative optimizationquality assessment

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Area of Science:

  • Medical image analysis
  • Ophthalmology
  • Computer vision

Background:

  • Fundus image quality is crucial for accurate disease diagnosis.
  • Diagnostic capability is a key metric for assessing fundus image quality (FIQ).
  • Existing methods often address these tasks independently.

Purpose of the Study:

  • To propose a dual-task collaborative optimization network (DTCONet) that explores the interplay between FIQ and disease diagnosis.
  • To enhance the performance of both tasks through mutual promotion.
  • To provide new insights into the relationship between FIQ assessment and disease diagnosis.

Main Methods:

  • Developed a dual-branch feature extraction framework to capture fine structures and pathological characteristics.
  • Designed a dual-task module for parallel processing of quality assessment and disease diagnosis using shared representations.
  • Introduced a collaborative optimization module to exploit the correlation between the two tasks.

Main Results:

  • The DTCONet demonstrated effectiveness in enhancing both fundus image quality assessment and disease diagnosis.
  • Experiments on five datasets confirmed the model's performance and generalizability.
  • The collaborative optimization approach showed significant improvements over independent task models.

Conclusions:

  • The proposed DTCONet effectively integrates quality assessment and disease diagnosis for improved medical image analysis.
  • The study highlights the synergistic relationship between FIQ and diagnostic performance.
  • DTCONet offers a promising approach for advancing automated fundus image analysis in clinical settings.