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.
Dht-net: Dy- namic hierarchical transformer network for liver and tumor segmentation.IEEE Journal of Biomedical and Health In- formatics
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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.