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Semi-supervised Long-tail Endoscopic Image Classification.

Run-Nan Cao1,2, Meng-Jie Fang1,2, Hai-Ling Li3

  • 1School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.

Chinese Medical Sciences Journal = Chung-Kuo I Hsueh K'O Hsueh Tsa Chih
|November 2, 2022
PubMed
Summary
This summary is machine-generated.

Semi-supervised learning (SSL) improves long-tail endoscopic image classification with limited data, especially when annotations are scarce. However, the pseudo-labeling strategy can worsen performance on underrepresented "tail" classes.

Keywords:
artificial intelligenceendoscopic imageimage classificationlong-tail distributionsemi-supervised learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Endoscopic image classification presents challenges due to limited annotated data, particularly for rare conditions (long-tail distribution).
  • Semi-supervised learning (SSL) offers a potential solution by leveraging unlabeled data to improve model performance.

Purpose of the Study:

  • To evaluate the effectiveness of the FixMatch SSL algorithm for long-tail endoscopic image classification using the HyperKvasir dataset.
  • To assess the impact of varying proportions of labeled data (20%, 50%, 100%) on classification performance.

Main Methods:

  • Applied the FixMatch SSL algorithm, utilizing consistency regularization and pseudo-labeling.
  • Trained and tested models on the HyperKvasir dataset, comprising 23 gastrointestinal classes.
  • Evaluated performance using micro-average, macro-average metrics, and Mathews Correlation Coefficient (MCC).

Main Results:

  • SSL improved overall classification performance (MCC) across all tested labeled data ratios (20%, 50%, 100%).
  • With 20% labeled data, SSL enhanced both micro and macro-average performance.
  • With 50% and 100% labeled data, SSL improved micro-average but degraded macro-average performance, indicating a bias towards majority classes.

Conclusions:

  • SSL is beneficial for long-tail endoscopic image classification, particularly with very limited labeled data, aiding diagnostic systems.
  • The pseudo-labeling component of SSL can exacerbate class imbalance, negatively impacting the classification of minority (tail) classes.