UIT-DarkCow team at ImageCLEFmedical Caption 2024: Diagnostic Captioning for Radiology Images Efficiency with Transformer Models

TL;DR

This paper presents Transformer-based models for automated diagnostic captioning of radiology images, demonstrating high-quality report generation that can improve clinical efficiency and accuracy.

Contribution

Introduction of Transformer encoder-decoder and Query Transformer architectures for radiology captioning, achieving top performance in the ImageCLEFmedical2024 challenge.

Findings

VisionDiagnostor-BioBART achieved highest BERTScore of 0.6267

Team DarkCow ranked third on the leaderboard

Models effectively generate high-quality diagnostic captions

Abstract

Purpose: This study focuses on the development of automated text generation from radiology images, termed diagnostic captioning, to assist medical professionals in reducing clinical errors and improving productivity. The aim is to provide tools that enhance report quality and efficiency, which can significantly impact both clinical practice and deep learning research in the biomedical field. Methods: In our participation in the ImageCLEFmedical2024 Caption evaluation campaign, we explored caption prediction tasks using advanced Transformer-based models. We developed methods incorporating Transformer encoder-decoder and Query Transformer architectures. These models were trained and evaluated to generate diagnostic captions from radiology images. Results: Experimental evaluations demonstrated the effectiveness of our models, with the VisionDiagnostor-BioBART model achieving the highest BERTScore of 0.6267. This performance contributed to our team, DarkCow, achieving third place on the leaderboard. Conclusion: Our diagnostic captioning models show great promise in aiding medical professionals by generating high-quality reports efficiently. This approach can facilitate better data processing and performance optimization in medical imaging departments, ultimately benefiting healthcare delivery.