Distribution-Aware Multi-Label FixMatch for Semi-Supervised Learning on CheXpert

• Main Authors: Ihler, Sontje, Kuhnke, Felix, Kuhlgatz, Timo, Seel, Thomas
• Format: Conference Proceeding
• Language: English
• Subjects: Filling; Imbalanced semi-supervised learning; Medical Image Analysis; Mirrors; Missing Labels; Multi-Label Classification; Pattern recognition; Performance gain; Robustness; Semi-supervised learning; Semi-supervised multi-label learning; Semisupervised learning; Solids; X-ray classification
• ISSN: 2160-7516
• DOI: 10.1109/CVPRW63382.2024.00235

Semi-supervised learning (SSL) has achieved remarkable success for multiclass classification in recent years, yielding a promising solution for medical image classification where labeled data is scarce but unlabeled images are accessible. In the context of multi-label problems however, SSL is still under-explored. In this work we adapt Fix-Match to the multi-label scenario, specifically focusing on CheXpert, a multi-label chest X-ray classification dataset which is imbalanced and only partially labeled. Leveraging distribution alignment, our proposed method, ML-FixMatch+DA, achieves solid performance gains in SSL tasks (AUC: +2.6%) and in a missing label scenario (AUC: +1.9%). In contrast to previous work we achieve a performance gain on CheXpert using FixMatch. We show that in contrast to multiclass FixMatch, where distribution alignment is optional, it is essential for multi-label FixMatch to handle class imbalance and generate reliable (positive and negative) pseudo-labels. Our pseudo-label selection is based on a single threshold for all classes and handles imbalance with no prior knowledge on label distributions. Our adaptation keeps the simplicity of the original multi-class FixMatch with no added hyperparameters (even for imbalanced data) and demonstrates the feasibility of simple SSL for multi-label problems, filling a crucial gap in the literature.