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)