AI RESEARCH
Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching
arXiv CS.CV
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ArXi:2507.22418v2 Announce Type: replace Quantifying aleatoric uncertainty in medical image segmentation is critical since it is a reflection of the natural variability observed among expert annotators. A conventional approach is to model the segmentation distribution using the generative model, but current methods limit the expression ability of generative models.