Applications of interpretable deep learning in neuroimaging: a comprehensive review

TL;DR

This comprehensive review examines how interpretable deep learning methods are applied in neuroimaging, highlighting current approaches, evaluation properties, and future research directions to improve trustworthiness and reliability.

Contribution

The paper systematically reviews neuroimaging applications of interpretable deep learning, categorizes methods, and critically analyzes explanation properties and their evaluation.

Findings

Seventy-five studies reviewed.

Ten categories of iDL methods identified.

Most popular approaches may be sub-optimal for neuroimaging.

Abstract

Clinical adoption of deep learning models has been hindered, in part, because the black-box nature of neural networks leads to concerns regarding their trustworthiness and reliability. These concerns are particularly relevant in the field of neuroimaging due to the complex brain phenotypes and inter-subject heterogeneity often encountered. The challenge can be addressed by interpretable deep learning (iDL) methods that enable the visualisation and interpretation of the inner workings of deep learning models. This study systematically reviewed the literature on neuroimaging applications of iDL methods and critically analysed how iDL explanation properties were evaluated. Seventy-five studies were included, and ten categories of iDL methods were identified. We also reviewed five properties of iDL explanations that were analysed in the included studies: biological validity, robustness, continuity, selectivity, and downstream task performance. We found that the most popular iDL approaches used in the literature may be sub-optimal for neuroimaging data, and we discussed possible future directions for the field.