April 12, 2024, 4:46 a.m. | Ke Zou, Yidi Chen, Ling Huang, Xuedong Yuan, Xiaojing Shen, Meng Wang, Rick Siow Mong Goh, Yong Liu, Huazhu Fu

cs.CV updates on arXiv.org arxiv.org

arXiv:2301.00349v2 Announce Type: replace-cross
Abstract: Medical image segmentation is critical for disease diagnosis and treatment assessment. However, concerns regarding the reliability of segmentation regions persist among clinicians, mainly attributed to the absence of confidence assessment, robustness, and calibration to accuracy. To address this, we introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks. DEviS not only enhances the calibration and robustness of baseline segmentation accuracy but also provides high-efficiency uncertainty estimation for reliable …

arxiv cs.cv eess.iv image medical segmentation type uncertainty

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