Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty
arxiv(2023)
摘要
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 predictions.
By leveraging subjective logic theory, we explicitly model probability and
uncertainty for the problem of medical image segmentation. Here, the Dirichlet
distribution parameterizes the distribution of probabilities for different
classes of the segmentation results. To generate calibrated predictions and
uncertainty, we develop a trainable calibrated uncertainty penalty.
Furthermore, DEviS incorporates an uncertainty-aware filtering module, which
utilizes the metric of uncertainty-calibrated error to filter reliable data
within the dataset. We conducted validation studies to assess both the accuracy
and robustness of DEviS segmentation, along with evaluating the efficiency and
reliability of uncertainty estimation. These evaluations were performed using
publicly available datasets including ISIC2018, LiTS2017, and BraTS2019.
Additionally, two potential clinical trials are being conducted at Johns
Hopkins OCT, Duke-OCT-DME, and FIVES datasets to demonstrate their efficacy in
filtering high-quality or out-of-distribution data. Our code has been released
in https://github.com/Cocofeat/DEviS.
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