DPN:Dynamics Priori Networks for Radiology Report Generation  

作　　者：Bokai Yang Hongyang Lei Huazhen Huang Xinxin Han Yunpeng Cai 

机构地区：[1]Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China [2]University of Chinese Academy of Sciences,Beijing 100049,China [3]School of Artificial Intelligence,Shenzhen Polytechnic University,Shenzhen 518055,China

出　　处：《Tsinghua Science and Technology》2025年第2期600-609,共10页清华大学学报自然科学版(英文版)

基　　金：supported by the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDB38050100);the Shenzhen Science and Technology Program(No.SGDX20201103095603009);the Shenzhen Polytechnic Research Fund(No.6023310009K).

摘　　要：Radiology report generation is of significant importance.Unlike standard image captioning tasks,radiology report generation faces more pronounced visual and textual biases due to constrained data availability,making it increasingly reliant on prior knowledge in this context.In this paper,we introduce a radiology report generation network termed Dynamics Priori Networks(DPN),which leverages a dynamic knowledge graph and prior knowledge.Concretely,we establish an adaptable graph network and harness both medical domain knowledge and expert insights to enhance the model’s intelligence.Notably,we introduce an image-text contrastive module and an image-text matching module to enhance the quality of the generated results.Our method is evaluated on two widely available datasets:X-ray collection from Indiana University(IU X-ray)and Medical Information Mart for Intensive Care,Chest X-Ray(MIMIC-CXR),where it demonstrates superior performance,particularly excelling in critical metrics.

关 键 词：radiology report generation dynamic knowledge graph prior knowledge contrastive learning 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]