Explainable Brain Imaging Scoring

Abstract

Brain image data analysis, based on voxel values, plays a crucial role in diagnosing and treating brain diseases. Recently, artificial intelligence techniques have been applied to this field, but challenges remain in improving processing speed and ensuring the interpretability of results. This paper focuses on scoring methods using dimensionality reduction. Appropriately derived scores can serve as brain image biomarkers, potentially lowering computational costs while enhancing explanatory power. Anatomical standardization, used as a preprocessing step, enables the application of matrix decomposition techniques. The multi-supervised sparse component analysis proposed here extends conventional matrix factorization by efficiently reducing large-scale brain image data through stepwise linear transformations. The inverse transformation helps identify relevant anatomical regions, thereby improving interpretability. Additionally, we demonstrate how to eliminate unnecessary variation by selectively inverting components of the multi-supervised method, using specific analytical examples.