About the Accuracy and Relevance of Deep Learning in a Challenging MRI-Image Classification Problem

Abstract

In this study a challenging binary classification was done with a large (>100GB, hundreds of subjects in total) annotated MRI-dataset in public a competition between over thousand teams with their proposals for the problem. For the classification an advanced deep neural network consisting of tailored structural elements having capabilities for detecting small abstract level features from the image data was developed and tested. The resulted ROC was 0,74 in the test data ($\mathrm{N}=87$) and 0,55 in the extended test data phase; the results were among other proposals (>1000 solutions) in the top 5-25%, correspondingly. The relevance and accuracy of the solution was discussed including a specific finding about interesting differences in the classification performance between the data from different types of MRI-scans, being in line with other independent research findings. This may indicate the results of the deep neural network provide some additional value about the presence of MGMT. However, in general level the topic is still open and requires further studies to achieve a better understanding.