Exploring the Effects of Contrastive Learning on Homogeneous Medical Image Data
- We investigate contrastive learning in a multi-task learning setting classifying and segmenting early Barrett’s cancer. How can contrastive learning be applied in a domain with few classes and low inter-class and inter-sample variance, potentially enabling image retrieval or image attribution? We introduce a data sampling strategy that mines per-lesion data for positive samples and keeps a queue of the recent projections as negative samples. We propose a masking strategy for the NT-Xent loss that keeps the negative set pure and removes samples from the same lesion. We show cohesion and uniqueness improvements of the proposed method in feature space. The introduction of the auxiliary objective does not affect the performance but adds the ability to indicate similarity between lesions. Therefore, the approach could enable downstream auto-documentation tasks on homogeneous medical image data.
Author: | Robert MendelORCiD, David Rauber, Christoph PalmORCiDGND |
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DOI: | https://doi.org/10.1007/978-3-658-41657-7 |
Parent Title (German): | Bildverarbeitung für die Medizin 2023: Proceedings, German Workshop on Medical Image Computing, July 2– 4, 2023, Braunschweig |
Publisher: | Springer Vieweg |
Place of publication: | Wiesbaden |
Document Type: | conference proceeding (article) |
Language: | English |
Year of first Publication: | 2023 |
Release Date: | 2023/06/19 |
First Page: | 128 |
Last Page: | 13 |
Institutes: | Fakultät Informatik und Mathematik |
Regensburg Center of Biomedical Engineering - RCBE | |
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC) | |
Begutachtungsstatus: | peer-reviewed |
research focus: | Lebenswissenschaften und Ethik |
Licence (German): | Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG |