Decision processes in 3D structural MRI schizophrenia classification evaluated with saliency maps.

Clinical decision support systems for psychiatric disorders such as schizophrenia can benefit from machine learning models based on neuroimaging data for objective diagnosis, prognosis, and effective treatment selection. Deep learning (DL) models promise to be suitable for this task since they can detect complex patterns in images without the need for prior information about candidate regions. Their downside, however, is the lack of transparency about the decision process. Explainable AI methods address this problem and might be helpful in the clinical translation of DL applications as well as potential biomarker indication. The current study qualitatively and quantitatively evaluates seven DL architectures frequently employed in medical image analyses with gradient-weighted class activation mapping (Grad-CAM) for plausibility and finds that only two of the seven models base their decisions in a schizophrenia classification task on plausible structural brain information, despite similar classification performance. Furthermore, we develop an approach to translate the saliency maps from the Grad-CAM into universally interpretable anatomical markers of schizophrenia and find candidate regions corresponding to known markers of schizophrenia. To conclude, this study demonstrates the necessity of using explainable methods alongside DL approaches and the feasibility to derive biomarkers with such methods.