Learning the Retinal Anatomy From Scarce Annotated Data Using Self-Supervised Multimodal Reconstruction

Abstract

[Abstract] Deep learning is becoming the reference paradigm for approaching many computer vision problems. Nevertheless, the training of deep neural networks typically requires a significantly large amount of annotated data, which is not always available. A proven approach to alleviate the scarcity of annotated data is transfer learning. However, in practice, the use of this technique typically relies on the availability of additional annotations, either from the same or natural domain. We propose a novel alternative that allows to apply transfer learning from unlabelled data of the same domain, which consists in the use of a multimodal reconstruction task. A neural network trained to generate one image modality from another must learn relevant patterns from the images to successfully solve the task. These learned patterns can then be used to solve additional tasks in the same domain, reducing the necessity of a large amount of annotated data.

In this work, we apply the described idea to the localization and segmentation of the most important anatomical structures of the eye fundus in retinography. The objective is to reduce the amount of annotated data that is required to solve the different tasks using deep neural networks. For that purpose, a neural network is pre-trained using the self-supervised multimodal reconstruction of fluorescein angiography from retinography. Then, the network is fine-tuned on the different target tasks performed on the retinography. The obtained results demonstrate that the proposed self-supervised transfer learning strategy leads to state-of-the-art performance in all the studied tasks with a significant reduction of the required annotations.