Fundus Image Classification and Retinal Disease Localization with Limited Supervision

Abstract

Image classification using deep convolutional neural networks (DCNN) has a competitive performance with other state-of-the-art methods. Fundus image classification into disease types is also a promising application domain of DCNN. Typically fundus image classifier is trained using fundus images with labels showing disease types. Such training data is relatively easy to obtain, but a massive number of training data is required to achieve adequate classification performance. If classifier can concentrate the evidential regions attached to training images, it is possible to boost the performance with a limited number of the training dataset. However, such regions are very hard to obtain, especially for fundus image classification because only professional ophthalmologist can give such regions and selecting such regions by GUI is very time-consuming. To boost the classification performance with significantly light ophthalmologist intervention, we propose a new method: first, we show evidential heatmaps by DCNN to ophthalmologists, and then obtained their feedback of selecting images with reasonable evidential regions. This intervention is far very easy for opthalmologist compared to drawing evidential regions. Experiments using fundus images revealed that our method improved accuracy from 90.1% to 94.5% in comparison with the existing method. We also found that the attention regions generated by our process are closer to the GT attention regions provided by ophthalmologists.