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

Area of Science:

• Neuroscience
• Artificial Intelligence
• Psychiatry

Background:

• Clinical decision support systems for psychiatric disorders like schizophrenia can be enhanced by machine learning models utilizing neuroimaging data.
• Deep learning (DL) models excel at detecting complex patterns in images without predefined region information, making them suitable for objective diagnosis, prognosis, and treatment selection.
• However, DL models often lack transparency in their decision-making processes, hindering clinical translation.

Purpose of the Study:

• To evaluate the plausibility of seven DL architectures in schizophrenia classification using neuroimaging data via gradient-weighted class activation mapping (Grad-CAM).
• To develop a method for translating Grad-CAM saliency maps into interpretable anatomical markers for schizophrenia.
• To demonstrate the necessity and feasibility of explainable AI (XAI) methods in deriving biomarkers for psychiatric disorders.

Main Methods:

• Qualitative and quantitative evaluation of seven DL architectures for schizophrenia classification.
• Application of gradient-weighted class activation mapping (Grad-CAM) to assess model decision-making.
• Development of an approach to translate saliency maps into interpretable anatomical markers.

Main Results:

• Only two out of seven evaluated DL models based their schizophrenia classification decisions on plausible structural brain information, despite comparable classification performance.
• The developed approach successfully translated saliency maps into interpretable anatomical markers.
• Candidate regions identified corresponded to known schizophrenia markers.

Conclusions:

• Explainable AI methods are essential for ensuring the clinical validity and interpretability of DL models in psychiatric neuroimaging.
• The study demonstrates the feasibility of using XAI techniques to derive objective, interpretable biomarkers for schizophrenia from neuroimaging data.
• This approach can aid in the clinical translation of DL applications and biomarker discovery for psychiatric disorders.