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.
Semi-supervised semantic segmentation with cross pseudo supervision, 2021
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Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms with unified dataset and deployment interfaces.
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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.
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APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms
Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms with unified dataset and deployment interfaces.