Ultrasound Report Generation with Multimodal Large Language Models for Standardized Texts
This work addresses the problem of automating ultrasound report generation for clinical workflows, though it is incremental as it builds on existing methods with specific improvements.
The study tackled the challenge of generating standardized ultrasound reports by proposing a unified framework for multi-organ and multilingual report generation, achieving relative gains of about 2% in BLEU, 3% in ROUGE-L, and 15% in CIDEr compared to the previous state-of-the-art method.
Ultrasound (US) report generation is a challenging task due to the variability of US images, operator dependence, and the need for standardized text. Unlike X-ray and CT, US imaging lacks consistent datasets, making automation difficult. In this study, we propose a unified framework for multi-organ and multilingual US report generation, integrating fragment-based multilingual training and leveraging the standardized nature of US reports. By aligning modular text fragments with diverse imaging data and curating a bilingual English-Chinese dataset, the method achieves consistent and clinically accurate text generation across organ sites and languages. Fine-tuning with selective unfreezing of the vision transformer (ViT) further improves text-image alignment. Compared to the previous state-of-the-art KMVE method, our approach achieves relative gains of about 2\% in BLEU scores, approximately 3\% in ROUGE-L, and about 15\% in CIDEr, while significantly reducing errors such as missing or incorrect content. By unifying multi-organ and multi-language report generation into a single, scalable framework, this work demonstrates strong potential for real-world clinical workflows.