Conditional flow matching produces segmentation samples whose pixel-wise variance quantifies aleatoric uncertainty in medical images by learning an exact density rather than relying on stochastic diffusion sampling.
In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17
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Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching
Conditional flow matching produces segmentation samples whose pixel-wise variance quantifies aleatoric uncertainty in medical images by learning an exact density rather than relying on stochastic diffusion sampling.