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In recent years, artificial intelligence (AI) systems have come to theforefront. These systems, mostly based on Deep learning (DL), achieve excellentresults in areas such as image processing, natural language processing, orspeech recognition. Despite the statistically high accuracy of deep learningmodels, their output is often a decision of "black box". Thus, Interpretabilitymethods have become a popular way to gain insight into the decision-makingprocess of deep learning models. Explanation of a deep learning model isdesirable in the medical domain since the experts have to justify theirjudgments to the patient. In this work, we proposed a method forexplanation-guided training that uses a Layer-wise relevance propagation (LRP)technique to force the model to focus only on the relevant part of the image.We experimentally verified our method on a convolutional neural network (CNN)model for low-grade and high-grade glioma classification problems. Ourexperiments show promising results in a way to use interpretation techniques inthe model training process.

Improving a neural network model by explanation-guided training for glioma classification based on MRI data