Figure 9:

Saliency maps by the EfficientNet-B2 model of the CT images of Figure 3 with regard to the “any” class. (A) Image shows the saliency map of the full-view image. (B–E) Images show the saliency maps of the images reconstructed from 512, 256, 128, and 64 views, respectively. (F–H) Images show the saliency maps of the images postprocessed by the U-Net of the corresponding sparse-view images. All maps were normalized via min-max normalization to range [0–1]. The rectangles are at the same position as in Figure 3, indicating the location of the present hemorrhages. Note: Saliency maps were generated by analyzing the gradients of the EfficientNet-B2 model with respect to the input. High values indicate that changes to those pixels have a substantial impact on the model's output, and therefore, those pixels are most important for the prediction.