2024/03/30 by Tongkun Su, Su, Tongkun, Jun Li +15 · 1 citation
Arts and Humanities · Health Professions · Psychology · #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #Digital Storytelling and Education #EFL/ESL Teaching and Learning #FOS: Computer and information sciences #Language, Metaphor, and Cognition
paper · pdf · doi:10.48550/arxiv.2404.00226
openalex publication_date 2024/03/30 · openalex created_date 2024/04/03 · openalex updated_date 2026/07/28
Multimodal pre-training demonstrates its potential in the medical domain, which learns medical visual representations from paired medical reports. However, many pre-training tasks require extra annotations from clinicians, and most of them fail to explicitly guide the model to learn the desired features of different pathologies. In this paper, we utilize Visual Question Answering (VQA) for multimodal pre-training to guide the framework focusing on targeted pathological features. We leverage descriptions in medical reports to design multi-granular question-answer pairs associated with different diseases, which assist the framework in pre-training without requiring extra annotations from experts. We also propose a novel pre-training framework with a quasi-textual feature transformer, a module designed to transform visual features into a quasi-textual space closer to the textual domain via a contrastive learning strategy. This narrows the vision-language gap and facilitates modality alignment. Our framework is applied to four downstream tasks: report generation, classification, segmentation, and detection across five datasets. Extensive experiments demonstrate the superiority of our framework compared to other state-of-the-art methods. Our code is available at https://github.com/MoramiSu/QFT-MICCAI2024.