Multimodal self-supervised learning for lesion localization

Multimodal deep learning utilizing imaging and diagnostic reports has made impressive progress in the field of medical imaging diagnostics, demonstrating a particularly strong capability for auxiliary diagnosis in cases where sufficient annotation information is lacking. Nonetheless, localizing diseases accurately without detailed positional annotations remains a challenge. Although existing methods have attempted to utilize local information to achieve fine-grained semantic alignment, their capability in extracting the fine-grained semantics of the comprehensive contextual within reports is limited. To solve this problem, we introduce a new method that takes full sentences from textual reports as the basic units for local semantic alignment. Our approach combines chest X-ray images with their corresponding textual reports, performing contrastive learning at both global and local levels. The leading results obtained by our method on multiple datasets confirm its efficacy in the task of lesion localization.