The successful application of deep learning-based methodologies is conditioned by the availability of sufficient annotated data, which is usually critical in medical applications. This has motivated the proposal of several approaches aiming to complement the training with reconstruction tasks over unlabeled input data, complementary broad labels, augmented datasets or data from other domains. In this work, we explore the use of reconstruction tasks over multiple medical imaging modalities as a more informative self-supervised approach. Experiments are conducted on multimodal reconstruction of retinal angiography from retinography. The results demonstrate that the detection of relevant domain-specific patterns emerges from this self-supervised setting.
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