Sun, Shichang, Yang, Haolin, Wang, Haibo, Du, Shun, Liu, Shuang and Lai, Yukun  ORCID: https://orcid.org/0000-0002-2094-5680
2026.
DACAS: Distortion-Aware Capacity Allocation for Style-augmented domain generalization in computational pathology.
Presented at: ICANN 2026,
Padua, Italy,
14-17 September 2026.
Published in: Pasa, L., Lintas, A., Tetko, I.V., Micheli, A., Navarin, N., Villa, A.E.P., Tortorella, D. and Polato, M. eds.
Artificial Neural Networks and Machine Learning – ICANN 2026.
Lecture Notes in Computer Science
(17092)
Springer Nature,
pp. 583-594.
10.1007/978-3-032-38407-2_46
|
Preview |
PDF
- Accepted Post-Print Version
Available under License Creative Commons Attribution. Download (464kB) | Preview |
Abstract
Data augmentation improves domain generalization by increasing distributional coverage, but may also introduce structured distortion that degrades semantic fidelity, especially in structure-sensitive tasks such as computational pathology. Existing approaches primarily address this trade-off by optimizing augmentation policies or softening supervision, implicitly treating all augmentation-induced variations as equally important in the learned representation. In this work, we revisit this problem from a representation perspective. We argue that augmentation reshapes the composition of information, requiring the representation to selectively encode these variations. As a result, the key challenge is not only which variations to generate, but which variations should be preferentially encoded and how representational resources should be allocated accordingly. To this end, we propose a distortion-aware capacity allocation framework that operates after augmentation. We introduce an adaptive discrete bottleneck to adapt representation granularity at the sample level, together with a region-aware distortion variable that modulates spatial encoding according to structural sensitivity. In this way, the model redistributes limited representational resources to preserve structure-critical information while absorbing less informative variations. Experiments on colorectal cancer histopathology show that the proposed approach consistently improves generalization under identical augmentation settings. These results highlight post-augmentation representation allocation as a key mechanism for handling heterogeneous distortion in domain generalization.
| Item Type: | Conference or Workshop Item - published (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computational & Mathematical Sciences Schools > Computer Science & Informatics |
| Additional Information: | RRS policy applied |
| Publisher: | Springer Nature |
| ISBN: | 9783032384065 |
| Date of First Compliant Deposit: | 8 October 2026 |
| Last Modified: | 08 Oct 2026 09:15 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/189741 |
Actions (repository staff only)
 |
Edit Item |
Altmetric
Altmetric