  Self-supervised learning in vision-language processing exploits semantic
alignment between imaging and text modalities. Prior work in biomedical VLP has
mostly relied on the alignment of single image and report pairs even though
clinical notes commonly refer to prior images. This does not only introduce
poor alignment between the modalities but also a missed opportunity to exploit
rich self-supervision through existing temporal content in the data. In this
work, we explicitly account for prior images and reports when available during
both training and fine-tuning. Our approach, named BioViL-T, uses a
CNN-Transformer hybrid multi-image encoder trained jointly with a text model.
It is designed to be versatile to arising challenges such as pose variations
and missing input images across time. The resulting model excels on downstream
tasks both in single- and multi-image setups, achieving state-of-the-art
performance on (I) progression classification, (II) phrase grounding, and (III)
report generation, whilst offering consistent improvements on disease
classification and sentence-similarity tasks. We release a novel multi-modal
temporal benchmark dataset, MS-CXR-T, to quantify the quality of
vision-language representations in terms of temporal semantics. Our
experimental results show the advantages of incorporating prior images and
reports to make most use of the data.
