Ultrasound Report Generation with Multimodal Large Language Models for Standardized Texts

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.

@article{ge2025_2505.08838,
  title={ Ultrasound Report Generation with Multimodal Large Language Models for Standardized Texts },
  author={ Peixuan Ge and Tongkun Su and Faqin Lv and Baoliang Zhao and Peng Zhang and Chi Hong Wong and Liang Yao and Yu Sun and Zenan Wang and Pak Kin Wong and Ying Hu },
  journal={arXiv preprint arXiv:2505.08838},
  year={ 2025 }
}