Extended Data Fig. 1: The GRAPE model and its interpretability analysis.
From: AI-based large-scale screening of gastric cancer from noncontrast CT imaging
a. Model workflow and architecture. The GRAPE model takes the input of a non-contrast CT scan and first segment the stomach with a U-Net to obtain the ROI of the stomach region. It then processes the ROI region with a joint segmentation and classification network which extracts the multi-level feature of a U-Net backbone and perform classification after global pooling (GP) and fully connected layers (FC). b. Examples of interpretability analysis and three GC cases. The GRAPE model outputs the localization of the detected GC and aligns well with its heatmap visualization via the Grad-CAM approach.
