Improving a neural network model by explanation-guided training for glioma classification based on MRI data
This work addresses the need for interpretable AI in medical diagnostics, specifically for glioma classification, but it appears incremental as it builds on existing LRP techniques.
The authors tackled the problem of improving neural network interpretability for glioma classification from MRI data by proposing an explanation-guided training method using Layer-wise relevance propagation (LRP) to focus on relevant image parts, resulting in promising experimental outcomes for low-grade and high-grade glioma classification.
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.