Details
Originalsprache | Englisch |
---|---|
Titel des Sammelwerks | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops |
Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
Seiten | 2295-2304 |
Seitenumfang | 10 |
ISBN (elektronisch) | 979-8-3503-6547-4 |
ISBN (Print) | 979-8-3503-6548-1 |
Publikationsstatus | Veröffentlicht - 16 Juni 2024 |
Publikationsreihe
Name | IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops |
---|---|
ISSN (Print) | 2160-7508 |
ISSN (elektronisch) | 2160-7516 |
Abstract
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. Institute of Electrical and Electronics Engineers Inc., 2024. S. 2295-2304 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Distribution-Aware Multi-Label FixMatch for Semi-Supervised Learning on CheXpert.
AU - Ihler, Sontje
AU - Kuhnke, Felix
AU - Kuhlgatz, Timo
AU - Seel, Thomas
PY - 2024/6/16
Y1 - 2024/6/16
N2 - 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.
AB - 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.
KW - semi supervised learning
KW - CheXpert
KW - multi-label classification
KW - Missing Labels
KW - X-ray classification
KW - Semi-supervised multi-label learning
KW - Semi-supervised learning
KW - Imbalanced semi-supervised learning
KW - Medical Image Analysis
KW - Multi-Label Classification
UR - http://www.scopus.com/inward/record.url?scp=85206483137&partnerID=8YFLogxK
U2 - 10.1109/CVPRW63382.2024.00235
DO - 10.1109/CVPRW63382.2024.00235
M3 - Conference contribution
SN - 979-8-3503-6548-1
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops
SP - 2295
EP - 2304
BT - Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
PB - Institute of Electrical and Electronics Engineers Inc.
ER -