Contrastive learning on diffusion-model features with foreground pixels selected by fusing class activation maps and diffusion gradient maps yields state-of-the-art weakly supervised medical image segmentation.
IEEE Transactions on Image Processing 30, 5875–5888 (2021)
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Contrastive Learning with Diffusion Features for Weakly Supervised Medical Image Segmentation
Contrastive learning on diffusion-model features with foreground pixels selected by fusing class activation maps and diffusion gradient maps yields state-of-the-art weakly supervised medical image segmentation.