  Recent studies have validated the association between cardiovascular disease
(CVD) risk and retinal fundus images. Combining deep learning (DL) and portable
fundus cameras will enable CVD risk estimation in various scenarios and improve
healthcare democratization. However, there are still significant issues to be
solved. One of the top priority issues is the different camera differences
between the databases for research material and the samples in the production
environment. Most high-quality retinography databases ready for research are
collected from high-end fundus cameras, and there is a significant domain
discrepancy between different cameras. To fully explore the domain discrepancy
issue, we first collect a Fundus Camera Paired (FCP) dataset containing
pair-wise fundus images captured by the high-end Topcon retinal camera and the
low-end Mediwork portable fundus camera of the same patients. Then, we propose
a cross-laterality feature alignment pre-training scheme and a self-attention
camera adaptor module to improve the model robustness. The cross-laterality
feature alignment training encourages the model to learn common knowledge from
the same patient's left and right fundus images and improve model
generalization. Meanwhile, the device adaptation module learns feature
transformation from the target domain to the source domain. We conduct
comprehensive experiments on both the UK Biobank database and our FCP data. The
experimental results show that the CVD risk regression accuracy and the result
consistency over two cameras are improved with our proposed method. The code is
available here:
\url{https://github.com/linzhlalala/CVD-risk-based-on-retinal-fundus-images}
