Abstract #0819

Explainable AI for CNN-based Prostate Tumor Segmentation in Multi-parametric MRI Correlated to Whole Mount Histopathology

Deepa Darshini Gunashekar¹, Lars Bielak^1,2, Arnie Berlin³, Leonard Hägele¹, Benedict Oerther⁴, Matthias Benndorf⁴, Anca Grosu^2,4, Constantinos Zamboglou^2,4, and Michael Bock^1,2

¹Dept.of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany, ²German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany, ³The MathWorks, Inc., Novi, MI, United States, ⁴Dept.of Radiology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany

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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