Clinical Report: Contextual Dual Learning Approaches for Semi-Supervised Classification of Medical Images
Overview
The proposed Hierarchical Semantic Calibration (HSC) framework enhances semi-supervised learning for medical image classification by leveraging contextual relationships among samples. HSC demonstrates significant improvements in accuracy, achieving 92.24% on NCT-CRC-HE and 94.17% on ISIC2018 with minimal labeled data.
Background
The challenge of limited labeled data in medical imaging necessitates innovative approaches like semi-supervised learning (SSL). Traditional pseudo-labeling methods often fail due to the ambiguity of early-stage lesions, leading to unreliable predictions. By utilizing the rich contextual information inherent in medical images, HSC aims to improve the reliability of pseudo-labeling.
Data Highlights
Dataset
Labeled Samples
Accuracy
Improvement
NCT-CRC-HE
200
92.24%
2.97%
ISIC2018
20% of data
94.17%
2.21%
Key Findings
HSC framework introduces local semantic neighborhood alignment to enhance pseudo-labeling reliability.
Global cluster-prototype calibration aligns class-level representations across augmented views.
Neighborhood-prototype consistency regularization adapts the alignment based on neighborhood compactness.
HSC outperforms state-of-the-art methods, including PEFAT, in accuracy with fewer labeled samples.
Utilizing contextual relationships improves the robustness of predictions in medical imaging.
Clinical Implications
The HSC framework offers a novel approach to improve the accuracy of medical image classification with limited labeled data. Clinicians and researchers can leverage this method to enhance diagnostic capabilities and reduce reliance on extensive expert annotations.
Conclusion
The Hierarchical Semantic Calibration framework represents a significant advancement in semi-supervised learning for medical images, providing a robust solution to the challenges of limited labeled data and enhancing classification accuracy.
