A two-stage framework adapts source models for cross-device meibomian gland segmentation using weak clinical priors and self-distillation, reaching Dice 0.716 on a 1000-to-100 image benchmark while enabling mask-free operation.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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TopoPult-SSL: Gland-Mask-Free Cross-Device Meibomian Gland Segmentation via Self-Distilled Weak Clinical Priors
A two-stage framework adapts source models for cross-device meibomian gland segmentation using weak clinical priors and self-distillation, reaching Dice 0.716 on a 1000-to-100 image benchmark while enabling mask-free operation.