Deep Auxiliary Learning via Bilevel Feature Optimization for Label-Efficient Medical Image Segmentation
Auxiliary learning (AL), which can improve the performance of a primary task by leveraging related auxiliary tasks, is commonly regarded as a special case of multi-task learning. Existing AL approaches typically require manually annotated auxiliary labels, increasing annotation costs, particularly in medical imaging where expert labeling is expensive. In this work, we investigate whether segmentation performance can be improved using auxiliary tasks whose labels are automatically derived from existing segmentation annotations, requiring no additional manual labeling. To address this, we propose a novel auxiliary learning framework, formulated as a bilevel optimization problem. Our method ...
| Authors | Fuping Wu, Xi Jia, Le Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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