Abstract: Weakly-supervised learning methods have become increasingly attractive for medical image segmentation, but suffered from a high dependence on quantifying the pixel-wise affinities of ...
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SAM3 ~160MB Auto-segment/Text 0.3-0.7s N/A SAM2.1_T (CPU) ~40MB Fast CPU N/A <1s SAM2.1_B (CPU) ~80MB Balanced CPU N/A 1-2s SAM2.1_L (CPU) ~224MB Quality CPU N/A 2-3s Select a class (Buildings, Water, ...
Abstract: Medical image segmentation has made significant strides with the development of basic models. Specifically, models that combine CNNs with transformers can successfully extract both local and ...
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