Region-specific Risk Quantification for Interpretable Prognosis of COVID-19
arxiv(2024)
摘要
The COVID-19 pandemic has strained global public health, necessitating
accurate diagnosis and intervention to control disease spread and reduce
mortality rates. This paper introduces an interpretable deep survival
prediction model designed specifically for improved understanding and trust in
COVID-19 prognosis using chest X-ray (CXR) images. By integrating a large-scale
pretrained image encoder, Risk-specific Grad-CAM, and anatomical region
detection techniques, our approach produces regional interpretable outcomes
that effectively capture essential disease features while focusing on rare but
critical abnormal regions. Our model's predictive results provide enhanced
clarity and transparency through risk area localization, enabling clinicians to
make informed decisions regarding COVID-19 diagnosis with better understanding
of prognostic insights. We evaluate the proposed method on a multi-center
survival dataset and demonstrate its effectiveness via quantitative and
qualitative assessments, achieving superior C-indexes (0.764 and 0.727) and
time-dependent AUCs (0.799 and 0.691). These results suggest that our
explainable deep survival prediction model surpasses traditional survival
analysis methods in risk prediction, improving interpretability for clinical
decision making and enhancing AI system trustworthiness.
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