Interpretable Gender Classification from Retinal Fundus Images Using BagNets

Deep neural networks (DNNs) are able to predict a person’s gender from retinal fundus images with high accuracy, even though this task is usually considered hardly possible by ophthalmologists. Therefore, it has been an open question which features allow reliable discrimination between male and female fundus images. To study this question, we used a particular DNN architecture called BagNet, which extracts local features from small image patches and then averages the class evidence across all patches. The BagNet performed on par with the more sophisticated Inception-v3 model, showing that the gender information can be read out from local features alone. BagNets also naturally provide saliency maps, which we used to highlight the most informative patches in fundus images. We found that most evidence was provided by patches from the optic disc and the macula, with patches from the optic disc providing mostly male and patches from the macula providing mostly female evidence. Although further research is needed to clarify the exact nature of this evidence, our results suggest that there are localized structural differences in fundus images between genders. Overall, we believe that BagNets may provide a compelling alternative to the standard DNN architectures also in other medical image analysis tasks, as they do not require post-hoc explainability methods.