  In recent years, artificial intelligence (AI) systems have come to the
forefront. These systems, mostly based on Deep learning (DL), achieve excellent
results in areas such as image processing, natural language processing, or
speech recognition. Despite the statistically high accuracy of deep learning
models, their output is often a decision of "black box". Thus, Interpretability
methods have become a popular way to gain insight into the decision-making
process of deep learning models. Explanation of a deep learning model is
desirable in the medical domain since the experts have to justify their
judgments to the patient. In this work, we proposed a method for
explanation-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. Our
experiments show promising results in a way to use interpretation techniques in
the model training process.
