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Human-Inspired Asymmetric Collaboration for Dual-Student Semi-Supervised Medical Image Segmentation
Dual-student learning enriches predictive diversity in SSMIS by introducing a strongly augmented view alongside a weakly augmented view. Insufficient strong augmentation may homogenize the students, whereas excessive augmentation can corrupt anatomical evidence and destabilize collaboration. This ch
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