Tumor-aware augmentation with task-guided attention analysis improves rectal cancer segmentation from magnetic resonance images
Although self-supervised pretraining is expected to learn broadly transferable representations, its effectiveness across imaging modalities substantially different from the pretraining domain, and on complex tumor-segmentation tasks, remains understudied. Evaluating CT-pretrained transformers on MRI rectal cancer segmentation, we identified two interacting failure modes in CT-to-MRI transfer: (a) inefficient token usage caused by zero-padding to match pretrained input dimensions, and (b) ineffective feature adaptation. We investigated these vulnerabilities using two primary CT-pretrained hierarchical shifted-window transformer backbones, SMIT and Swin UNETR, together with VoCo as a large-...
| Authors | Aneesh Rangnekar, Joao Miranda, Natally Horvat et al. |
|---|---|
| Primary category | eess.IV |
| Published | 6 May 2026 |
| Last updated at source | 24 August 2026 |
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