  Instead of using current deep-learning segmentation models (like the UNet and
variants), we approach the segmentation problem using trained Convolutional
Neural Network (CNN) classifiers, which automatically extract important
features from images for classification. Those extracted features can be
visualized and formed into heatmaps using Gradient-weighted Class Activation
Mapping (Grad-CAM). This study tested whether the heatmaps could be used to
segment the classified targets. We also proposed an evaluation method for the
heatmaps; that is, to re-train the CNN classifier using images filtered by
heatmaps and examine its performance. We used the mean-Dice coefficient to
evaluate segmentation results. Results from our experiments show that heatmaps
can locate and segment partial tumor areas. But use of only the heatmaps from
CNN classifiers may not be an optimal approach for segmentation. We have
verified that the predictions of CNN classifiers mainly depend on tumor areas,
and dark regions in Grad-CAM's heatmaps also contribute to classification.
