# Ultrasound Report Generation with Multimodal Large Language Models for Standardized Texts

arXiv: https://arxiv.org/abs/2505.08838 · HTML: https://mlsanity.com/paper/2505.08838/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 13 May 2025.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale](https://mlsanity.com/paper/2406.19280/) (2024)
- [Automated Radiology Report Generation: A Review of Recent Advances](https://mlsanity.com/paper/2405.10842/) (2024)
- [Ultrasound Report Generation with Cross-Modality Feature Alignment via Unsupervised Guidance](https://mlsanity.com/paper/2406.00644/) (2024)
- [A Self-Guided Framework for Radiology Report Generation](https://mlsanity.com/paper/2206.09378/) (2022)
- [DeltaNet:Conditional Medical Report Generation for COVID-19 Diagnosis](https://mlsanity.com/paper/2211.13229/) (2022)
- [Breast Ultrasound Report Generation using LangChain](https://mlsanity.com/paper/2312.03013/) (2023)

## Built on by (most cited first, 1 of 1)

- [MedVQA-TREE: A Multimodal Reasoning and Retrieval Framework for Sarcopenia Prediction](https://mlsanity.com/paper/2508.19319/) (2025)

## Abstract (arXiv)

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.