Fig. 1: Study workflow.

A The training process of the model. This deep learning training process includes two branches: supervised training using data with expert diagnosis results and unsupervised training using data from other centres without expert diagnosis results. a ROI extraction. LST toolbox was used to automatically segment WMH. Two senior neurologists corrected manually. b RFs extraction: Engineered RFs were extracted first, and then a transformer architecture was used to extract DL RFs. The blue bar represents the extracted engineered RFs. The grey bar represents the zero paddings. The green bar represents the positional embeddings. The red bar represents the DL RFs. c CI prediction: DL RFs were used to predict CI. d Domain adaptation. The domain discriminator was used to discriminate DL RFs from the source and the target domains, which enables the transformer models to learned to extract DL RFs that are both discriminative and invariant to the change of domains. B The external verification process of the model. Two independent cohorts from the Zheer and Xianlin communities were verified in the above model in a supervised manner. The output classification results were compared to expert annotations. e Grad-CAM. Grad-CAM uses the gradient information flowing into the Norm layer of the penultimate Transformer block to produce a heatmap highlighting the important RFs that correspond to the decision of the model. CI cognitive impairment, DL deep learning, Grad-CAM gradient-weighted class activation mapping, RFs radiomic features, ROI area of interest, WMH white matter hyperintensity. The figure was created using Microsoft PowerPoint.