Align, Reason and Learn: Enhancing Medical Vision-and-Language Pre-training with Knowledge

TL;DR

This paper introduces a knowledge-enhanced medical vision-and-language pre-training method that aligns, reasons, and learns from structured medical knowledge, significantly improving performance on multiple downstream tasks.

Contribution

It systematically incorporates structured medical knowledge into Med-VLP by aligning representations, enabling reasoning, and emphasizing critical information, which was lacking in prior methods.

Findings

Achieves state-of-the-art results on all downstream tasks.

Effectively integrates medical knowledge into vision-and-language models.

Provides a comprehensive benchmark for future research.

Abstract

Medical vision-and-language pre-training (Med-VLP) has received considerable attention owing to its applicability to extracting generic vision-and-language representations from medical images and texts. Most existing methods mainly contain three elements: uni-modal encoders (i.e., a vision encoder and a language encoder), a multi-modal fusion module, and pretext tasks, with few studies considering the importance of medical domain expert knowledge and explicitly exploiting such knowledge to facilitate Med-VLP. Although there exist knowledge-enhanced vision-and-language pre-training (VLP) methods in the general domain, most require off-the-shelf toolkits (e.g., object detectors and scene graph parsers), which are unavailable in the medical domain. In this paper, we propose a systematic and effective approach to enhance Med-VLP by structured medical knowledge from three perspectives. First, considering knowledge can be regarded as the intermediate medium between vision and language, we align the representations of the vision encoder and the language encoder through knowledge. Second, we inject knowledge into the multi-modal fusion model to enable the model to perform reasoning using knowledge as the supplementation of the input image and text. Third, we guide the model to put emphasis on the most critical information in images and texts by designing knowledge-induced pretext tasks. To perform a comprehensive evaluation and facilitate further research, we construct a medical vision-and-language benchmark including three tasks. Experimental results illustrate the effectiveness of our approach, where state-of-the-art performance is achieved on all downstream tasks. Further analyses explore the effects of different components of our approach and various settings of pre-training.