CVPR 2023 - Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation

In this episode we discuss Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation by Mingjie Li, Bingqian Lin, Zicong Chen, Haokun Lin, Xiaodan Liang, Xiaojun Chang. The paper proposes a knowledge graph with dynamic structure and nodes to enhance automatic radiology reporting. Existing models that use medical knowledge graphs have limited effectiveness because they have fixed structures that don't update during training. The proposed model, named DCL, allows for the addition of specific knowledge extracted from retrieved reports in a bottom-up manner, integrating each image feature with an updated graph. The model also introduces image-report contrastive and image-report matching losses to better represent visual features and textual information. Evaluation on two datasets shows that DCL outperforms previous state-of-the-art models.