Component-Adaptive and Lesion-Level Supervision for Improved Small Structure Segmentation in Brain MRI
Small lesions in brain MRI are hard to segment because they occupy a tiny fraction of the volume and are dominated by background and larger lesions during voxel-wise optimization, so a model can reach a high Dice similarity coefficient (DSC) while missing many of them. We propose CATMIL, a training objective that adds two auxiliary terms to the standard nnU-Net Dice and cross-entropy loss without changing the architecture. The Component-Adaptive Tversky (CAT) term weights lesion voxels by the inverse size of their connected component, so each lesion contributes nearly equally regardless of volume. The lesion-level Multiple Instance Learning (MIL) term treats each lesion as a bag of voxels...
| Authors | Minh Sao Khue Luu, Evgeniy N. Pavlovskiy, Bair N. Tuchinov |
|---|---|
| Primary category | cs.CV |
| Published | 9 April 2026 |
| Last updated at source | 9 October 2026 |
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