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.

通过对神经影像学应用的可解释性深度学习方法的文献进行系统回顾和评估，本研究发现最受欢迎的可解释性深度学习方法在神经影像数据上可能不太理想，并探讨了该领域的可能未来方向。