Distribution-Aware Multi-Label FixMatch for Semi-Supervised Learning on CheXpert.
- verfasst von
- Sontje Ihler, Felix Kuhnke, Timo Kuhlgatz, Thomas Seel
- Abstract
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.
- Organisationseinheit(en)
-
Institut für Mechatronische Systeme
Institut für Informationsverarbeitung
- Typ
- Aufsatz in Konferenzband
- Seiten
- 2295-2304
- Anzahl der Seiten
- 10
- Publikationsdatum
- 16.06.2024
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Maschinelles Sehen und Mustererkennung, Elektrotechnik und Elektronik
- Elektronische Version(en)
-
https://doi.org/10.1109/CVPRW63382.2024.00235 (Zugang:
Geschlossen)