MedICaT: A Dataset of Medical Images, Captions, and Textual References
This addresses the challenge of figure retrieval and alignment in biomedical documents for researchers and AI systems, but it is incremental as it builds on prior work by focusing on medical data and new tasks.
The authors tackled the problem of understanding complex medical figures in scientific papers by introducing MedICaT, a dataset of 217K medical images with captions and textual references, which enabled tasks like subfigure-to-subcaption alignment and improved image-text matching.
Understanding the relationship between figures and text is key to scientific document understanding. Medical figures in particular are quite complex, often consisting of several subfigures (75% of figures in our dataset), with detailed text describing their content. Previous work studying figures in scientific papers focused on classifying figure content rather than understanding how images relate to the text. To address challenges in figure retrieval and figure-to-text alignment, we introduce MedICaT, a dataset of medical images in context. MedICaT consists of 217K images from 131K open access biomedical papers, and includes captions, inline references for 74% of figures, and manually annotated subfigures and subcaptions for a subset of figures. Using MedICaT, we introduce the task of subfigure to subcaption alignment in compound figures and demonstrate the utility of inline references in image-text matching. Our data and code can be accessed at https://github.com/allenai/medicat.