The article presents a Deep Evidential Segmentation (DEviS) model that aims to achieve reliable and robust medical image segmentation. The key highlights are:
DEviS derives probabilities and uncertainties for different class segmentation problems using Subjective Logic (SL) theory, where the Dirichlet distribution parameterizes the distribution of probabilities for different classes.
DEviS develops a trainable Calibrated Uncertainty Penalty (CUP) to generate more calibrated confidence and maintain the segmentation performance of the base network.
DEviS incorporates an Uncertainty-Aware Filtering (UAF) strategy to facilitate the translation of DEviS into clinical applications, enabling the detection of out-of-distribution (OOD) data and providing insights into image data quality.
Extensive experiments on three publicly available datasets (ISIC2018, LiTS2017, and BraTS2019) demonstrate that DEviS can achieve accurate and robust segmentation performance under various degraded conditions (noise, blur, and random masking), while also providing efficient and reliable uncertainty estimation.
Two potential clinical applications involving OOD data detection and image data quality indicators are explored, further showcasing the practical utility of the proposed DEviS framework.
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by Ke Zou,Yidi ... at arxiv.org 04-12-2024
https://arxiv.org/pdf/2301.00349.pdfDeeper Inquiries