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Article overview
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EvidenceCap: Towards trustworthy medical image segmentation via evidential identity cap | | Ke Zou
; Xuedong Yuan
; Xiaojing Shen
; Yidi Chen
; Meng Wang
; Rick Siow Mong Goh
; Yong Liu
; Huazhu Fu
; | | Date: |
1 Jan 2023 | | Abstract: | Medical image segmentation (MIS) is essential for supporting disease
diagnosis and treatment effect assessment. Despite considerable advances in
artificial intelligence (AI) for MIS, clinicians remain skeptical of its
utility, maintaining low confidence in such black box systems, with this
problem being exacerbated by low generalization for out-of-distribution (OOD)
data. To move towards effective clinical utilization, we propose a foundation
model named EvidenceCap, which makes the box transparent in a quantifiable way
by uncertainty estimation. EvidenceCap not only makes AI visible in regions of
uncertainty and OOD data, but also enhances the reliability, robustness, and
computational efficiency of MIS. Uncertainty is modeled explicitly through
subjective logic theory to gather strong evidence from features. We show the
effectiveness of EvidenceCap in three segmentation datasets and apply it to the
clinic. Our work sheds light on clinical safe applications and explainable AI,
and can contribute towards trustworthiness in the medical domain. | | Source: | arXiv, 2301.00349 | | Services: | Forum | Review | PDF | Favorites | |
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