End-to-end multi-task learning for simultaneous optic disc and cup segmentation and glaucoma classification in eye fundus images

The automated analysis of eye fundus images is crucial towards facilitating the screening and early diagnosis of glaucoma. Nowadays, there are two common alternatives for the diagnosis of this disease using deep neural networks. One is the segmentation of the optic disc and cup followed by the morphological analysis of these structures. The other is to directly address the diagnosis as an image classification task. The segmentation approach presents the advantage of using pixel-level labels with precise morphological information for training. However, while this detailed training feedback is not available for the classification approach, in this case the image-level labels may allow the discovery of additional non-morphological cues that are also relevant for the diagnosis. In this work, we propose a novel multi-task approach for the simultaneous classification of glaucoma and segmentation of the optic disc and cup. This approach can improve the overall performance by taking advantage of both pixel-level and image-level labels during the network training. Additionally, the segmentation maps that are predicted together with the diagnosis allow the extraction of relevant biomarkers such as the cup-to-disc ratio. The proposed methodology presents two relevant technical novelties. First, a network architecture for simultaneous segmentation and classification that increases the number of shared parameters between both tasks. Second, a multi-adaptive optimization strategy that ensures that both tasks contribute similarly to the parameter updates during training, avoiding the use of loss weighting hyperparameters. To validate our proposal, an exhaustive experimentation was performed on the public REFUGE and DRISHTI-GS datasets. The results show that our proposal outperforms comparable multi-task baselines and is highly competitive with existing state-of-the-art approaches. Additionally, the provided ablation study shows that both the network architecture and the optimization approach are independently advantageous for multi-task learning.