KDMG: knowledge-enhanced dynamic memory and gated fusion for chest X-ray report generation

In order to overcome the challenges of incorrect medical terminology application and inaccurate descriptions in the generated reports due to the static character of medical knowledge and rough features combination, this paper will introduce a novel method in the form of the KDMG model of chest X-ray report generation to reduce these difficulties. KDMG has two main innovations: 1) the Knowledge-Enhanced Dynamic Memory module, which constructs a structured medical database and uses confidence-aware querying and momentum update mechanisms to facilitate dynamic, personalized retrieval of medical knowledge based on static priors; 2) the Gated Residual Feature Fusion module, which emulates the reasoning process of doctors who first view images and subsequently make a judgment, incorporating knowledge into visual features using a spatially adaptable gated network. Experiments on the IU X-Ray and MIMIC-CXR datasets demonstrate that KDMG achieves near state-of-the-art performance across both text generation metrics, such as BLEU-4 and CIDEr, and clinical accuracy metrics, including CheXpert F1 Score. Furtherrmore, ablation studies and fusion strategy comparisons further validate the effectiveness and design rationale of each module.