Keynote

Title: Automated Medical Report Generation and Visual Question Answering

Abstract: The rapid growth of medical imaging data has far outpaced the availability of trained radiologists, significantly increasing their workload. To alleviate this burden, reduce diagnostic errors, and streamline clinical workflows, the need for automated medical diagnostic report generation has become more urgent than ever. However, this task is particularly challenging, as it requires the ability to capture and describe clinically significant fine-grained visual differences in highly similar medical images. Additionally, critical disease-related keywords can easily be overshadowed by the prevalence of similar phrases describing common image content. Moreover, generating comprehensive reports that detail both normal and pathological findings within images adds to the complexity.  In this talk, I will showcase our latest research on automated medical diagnostic report generation and medical visual question answering, highlighting how we have tackled these challenges. Our work has transitioned from traditional encoder-decoder models to cutting-edge approaches utilizing large language models (LLMs). I will also discuss the current limitations of these methods and propose potential future directions.