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.
In: International Conference on Medical Image Computing and Computer-Assisted Intervention
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.