Probing Intrinsic Medical Task Relationships: A Contrastive Learning Perspective
While much of the medical computer vision community has focused on advancing performance for specific tasks, the underlying relationships between tasks, i.e., how they relate, overlap, or differ on a representational level, remain largely unexplored. Our work explores these intrinsic relationships between medical vision tasks, specifically, we investigate 30 tasks, such as semantic tasks (e.g., segmentation and detection), image generative tasks (e.g., denoising, inpainting, or colorization), and image transformation tasks (e.g., geometric transformations). Our goal is to probe whether a data-driven representation space can capture an underlying structure of tasks across a variety of 39 d...
| Authors | Jonas Muth, Zdravko Marinov, Simon Reiß |
|---|---|
| Primary category | cs.CV |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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