Deep Learning, Artificial Intelligence, Medical Imaging, Image Classification, Biomedical Segmentation.
used, hence for the S setting, the network predicts random outputs. For S+C and S+C∗ settings, s=0% reduces to a classifier which predicts one value per patch. 4. Experimental Results 4.1. Classification Though classification accuracy is not the focus, they also perform the classification