  Contrastive Language--Image Pre-training (CLIP) has shown remarkable success
in learning with cross-modal supervision from extensive amounts of image--text
pairs collected online. Thus far, the effectiveness of CLIP has been
investigated primarily in general-domain multimodal problems. This work
evaluates the effectiveness of CLIP for the task of Medical Visual Question
Answering (MedVQA). To this end, we present PubMedCLIP, a fine-tuned version of
CLIP for the medical domain based on PubMed articles. Our experiments are
conducted on two MedVQA benchmark datasets and investigate two MedVQA methods,
MEVF (Mixture of Enhanced Visual Features) and QCR (Question answering via
Conditional Reasoning). For each of these, we assess the merits of visual
representation learning using PubMedCLIP, the original CLIP, and
state-of-the-art MAML (Model-Agnostic Meta-Learning) networks pre-trained only
on visual data. We open source the code for our MedVQA pipeline and
pre-training PubMedCLIP. CLIP and PubMedCLIP achieve improvements in comparison
to MAML's visual encoder. PubMedCLIP achieves the best results with gains in
the overall accuracy of up to 3%. Individual examples illustrate the strengths
of PubMedCLIP in comparison to the previously widely used MAML networks. Visual
representation learning with language supervision in PubMedCLIP leads to
noticeable improvements for MedVQA. Our experiments reveal distributional
differences in the two MedVQA benchmark datasets that have not been imparted in
previous work and cause different back-end visual encoders in PubMedCLIP to
exhibit different behavior on these datasets. Moreover, we witness fundamental
performance differences of VQA in general versus medical domains.
