Integrating MedCLIP and Cross-Modal Fusion for Automatic Radiology Report Generation

TL;DR

This paper introduces a novel cross-modal framework combining MedCLIP and fusion techniques to enhance automatic radiology report generation, improving report quality and relevance using attention-based feature extraction and retrieval.

Contribution

The proposed method uniquely integrates MedCLIP for report retrieval and feature extraction with a fusion module, advancing the accuracy and coherence of automated radiology reports.

Findings

Improved report quality and relevance on IU-Xray dataset

Effective use of MedCLIP for report retrieval and feature extraction

Ablation studies confirm the importance of retrieval and feature integration

Abstract

Automating radiology report generation can significantly reduce the workload of radiologists and enhance the accuracy, consistency, and efficiency of clinical documentation.We propose a novel cross-modal framework that uses MedCLIP as both a vision extractor and a retrieval mechanism to improve the process of medical report generation.By extracting retrieved report features and image features through an attention-based extract module, and integrating them with a fusion module, our method improves the coherence and clinical relevance of generated reports.Experimental results on the widely used IU-Xray dataset demonstrate the effectiveness of our approach, showing improvements over commonly used methods in both report quality and relevance.Additionally, ablation studies provide further validation of the framework, highlighting the importance of accurate report retrieval and feature integration in generating comprehensive medical reports.