Fig. 5

Network attention gates (AGs) and class activation maps (CAMs) visualizations. An example is network visualization for nodule prediction (first row) and lung fibrosis prediction (second row) tasks. The first, second, third, and fourth columns indicate the ground-truth (GT) CT image, first attention gate (AG-1) at No. 11 layer depth, second attention gate (AG-2) at No. 14 layer depth, and the class activation map (CAM) at the final layer, respectively. The fifth column indicates the nodule ground-truth mask (GT Mask), which is not available when the model was trained. The case demonstrated here is a benign nodule in the non-fibrotic lung, where both nodule malignancy and fibrosis models made the correct inferences. From the AGs and CAMs, we can observe the nodule network focuses on nodule parenchyma and its surrounding tissues, while the fibrosis network focuses on other lung tissue with the nodule parenchyma excluded