Vision-Language Modelling For Radiological Imaging and Reports In The Low Data Regime

Understand

This paper explores training medical vision-language models (VLMs) -- where the visual and language inputs are embedded into a common space -- with a particular focus on scenarios where training data is limited, as is often the case in clinical datasets.

• We explore several candidate methods to improve low-data performance, including: (i) adapting generic pre-trained models to novel image and text domains (i.e.
• medical imaging and reports) via unimodal self-supervision; (ii) using local (e.g.
• GLoRIA) & global (e.g.