Application of belief functions to medical image segmentation: A review. (arXiv:2205.01733v1 [cs.CV])

Web: http://arxiv.org/abs/2205.01733

Belief function theory, a formal framework for uncertainty analysis and
multiple evidence fusion, has made significant contributions in the medical
domain, especially since the development of deep learning. Medical image
segmentation with belief function theory has shown significant benefits in
clinical diagnosis and medical image research. In this paper, we provide a
review of medical image segmentation methods using belief function theory. We
classify the methods according to the fusion step and explain how information
with uncertainty or imprecision is …

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