Comparison of regions of interest by DiDA and Grad-CAM for age estimation models from fundus image

Abstract

Machine learning has been shown to accurately estimate true age from fundus images. However, it is currently unclear which features the machine learning model uses to make these determinations, and which parts of the image are most clinically relevant for age estimation is also unknown. While methods such as Grad-CAM and DiDA can be used to interpret where the machine learning model is making inferences, most studies have focused on object detection and classification, with few investigating regression problems such as age estimation. In this paper, we applied Grad-CAM and DiDA to age estimation from fundus images and investigated where the machine learning model made age estimates. We found a common response of Grad-CAM and DiDA in approximately 80% of the images, with areas that were masked by DiDA showing lower estimated ages. This suggests that the machine learning model considers these areas as important factors in age estimation, and that they contribute to higher true age estimates.