Vision foundation models quantify aleatoric uncertainty via feature diversity and singular value energy to enable uncertainty-aware data filtering and dynamic training optimization for improved medical image segmentation.
Segvol: Universal and interactive volumetric medical image segmen- tation.Advances in Neural Information Processing Systems, 37:110746–110783
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Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models
Vision foundation models quantify aleatoric uncertainty via feature diversity and singular value energy to enable uncertainty-aware data filtering and dynamic training optimization for improved medical image segmentation.