A lightweight SAM student trained with MSE plus perceptual loss reaches SAM-like Dice scores on some medical datasets but falls behind on breast ultrasound, with no error bars or prompt details.
However, the significant computational demands of SAM hinder its deployment in real-time and resource-constrained environ- ments, such as mobile devices and edge platforms
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Efficient Knowledge Distillation of SAM for Medical Image Segmentation
A lightweight SAM student trained with MSE plus perceptual loss reaches SAM-like Dice scores on some medical datasets but falls behind on breast ultrasound, with no error bars or prompt details.