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Class-Specific Distribution Alignment for semi-supervised medical image classification.

Zhongzheng Huang¹, Jiawei Wu², Tao Wang³

¹Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China; College of Computer and Data Science, Fuzhou University, Fuzhou, China.

Computers in Biology and Medicine

|July 30, 2023

Summary

This study introduces Class-Specific Distribution Alignment (CSDA), a novel semi-supervised learning method that effectively handles imbalanced medical image datasets. CSDA improves classification accuracy by aligning class distributions and balancing data for better disease detection.

Keywords:

Distribution alignment Medical image classification Self-training Semi-supervised learning

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Area of Science:

Artificial Intelligence
Medical Imaging
Machine Learning

Background:

Deep neural networks excel in medical image classification but face challenges with time-consuming data annotation and imbalanced datasets.
The scarcity of certain diseases leads to skewed data distributions, hindering model performance.

Purpose of the Study:

To propose a semi-supervised learning framework, Class-Specific Distribution Alignment (CSDA), designed for highly imbalanced medical image datasets.
To address the bias towards majority classes inherent in imbalanced data.

Main Methods:

Developed CSDA, a semi-supervised learning framework based on self-training.
Introduced a new perspective on distribution alignment as a change of basis in vector space.
Proposed a Variable Condition Queue (VCQ) module to maintain balanced unlabeled samples per class.

Main Results:

CSDA demonstrates competitive performance in semi-supervised classification tasks.
The method was evaluated on HAM10000 (skin disease), CheXpert (thoracic disease), and Kvasir (endoscopic images).

Conclusions:

CSDA offers an effective approach to semi-supervised learning for imbalanced medical image classification.
The framework successfully mitigates bias towards majority classes, improving overall model accuracy.