TCSA: A Text-Guided Cross-View Medical Semantic Alignment Framework for Adaptive Multi-view Visual Representation Learning

Recently, in the medical domain, visual-language (VL) representation learning has demonstrated potential effectiveness in diverse medical downstream tasks. However, existing works typically pre-trained on the one-to-one corresponding medical image-text pairs, disregarding fluctuation in the quantity of views corresponding to reports (e.g., chest X-rays typically involve 1 to 3 projection views). This limitation results in sub-optimal performance in scenarios with varying quantities of views (e.g., arbitrary multi-view classification). To address this issue, we propose a novel Text-guided Cross-view Semantic Alignment (TCSA) framework for adaptive multi-view visual representation learning. For arbitrary number of multiple views, TCSA learns view-specific private latent sub-spaces and then maps them to a scale-invariant common latent sub-space, enabling individual treatment of arbitrary view type and normalization of arbitrary quantity of views to a consistent scale in the common sub-space. In the private sub-spaces, TCSA leverages word context as guidance to match semantic corresponding sub-regions across multiple views via cross-modal attention, facilitating alignment of different types of views in the private sub-space. This promotes the combination of information from arbitrary multiple views in the common sub-space. To the best of our knowledge, TCSA is the first VL framework for arbitrary multi-view visual representation learning. We report the results of TCSA on multiple external datasets and tasks. Compared with the state of the art frameworks, TCSA achieves competitive results and generalize well to unseen data.