An explainable deep learning model was implemented to interpret the predictions of a convolution neural network (CNN) for prostate tumor segmentation. The CNN automatically segments the prostate gland and prostate tumors in multi-parametric MRI data using co-registered whole mount histopathology images as ground truth. For the interpretation of the CNN, saliency maps are generated by generalizing the Gradient Weighted Class Activation Maps method for prostate tumor segmentation. Evaluations on the saliency method indicate that the CNN was able to correctly localize the tumor and the prostate by targeting the pixels in the image deemed important for the CNN's prediction.
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