Unified Mixture-of-Experts Framework for Joint Cardiac and Vascular Ultrasound Analysis and Report Generation
Bin Pu, Jiewen Yang, Xingguo Lv, Kai Xu, Kenli Li
Abstract
Echocardiography and vascular ultrasound are essential for comprehensive cardiovascular assessment, yet manual evaluation and writing reports are labor-intensive, time-consuming, and require expertise from both cardiology and vascular surgery departments. Current automated report generation systems mainly focus on X-ray or CT, often neglecting echocardiographic modalities and critical quantitative parameters like aortic diameter and main pulmonary artery diameter, limiting their clinical utility. Moreover, the interdependence between cardiac and peripheral vascular health necessitates cross-departmental insights, which existing methods fail to incorporate. To address these limitations, we first propose the vision-language framework named the Echo-Cardiac-Vascular (ECV), for joint cardiac and vascular ultrasound report generation and parameter measurements. ECV introduces a Mixture-of-Experts vision encoder tailored for distinct ultrasound subtypes, a structured parameter measurement module for accurate quantification, and task-specific decoders that generate interpretable, multimodal diagnostic reports. Our framework, trained on 10K+ paired records, achieves high accuracy, improving diagnostic efficiency, consistency, and cross-disciplinary clinical applicability.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 4a649881-b6e7-422d-baa5-e55448fe133bBuilds on9
- LoRA: Low-Rank Adaptation of Large Language ModelsEdward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu et al.ICLR 2022 · 18,833 citations
- MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language ModelsDeyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li et al.ICLR 2024 · 3,079 citations
- Generating Radiology Reports via Memory-driven TransformerZhihong Chen, Yan Song, Tsung-Hui Chang, Xiang WanEMNLP 2020 · 552 citations
- Clinical-BERT: Vision-Language Pre-training for Radiograph Diagnosis and Reports GenerationBin Yan, Mingtao PeiAAAI 2022 · 138 citations
- CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question AnsweringTianyu Huai, Jie Zhou, Xingjiao Wu, Qin Chen et al.CVPR 2025
Related papers
- EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal Ultrasound IntelligenceChaoyin She, Ruifang Lu, Lida Chen, Wei Wang et al.ACL 2026 · 8 citations
- DiA-gnostic VLVAE: Disentangled Alignment-Constrained Vision Language Variational AutoEncoder for Robust Radiology Reporting with Missing ModalitiesNagur Shareef Shaik, Teja Krishna Cherukuri, Adnan Masood, Dong Hye YeAAAI 2026
- Organ-Aware Routing Mixture-of-Retrieval Augmented Generation for Fetal Ultrasound ReportingBin Pu, Siyu Wang, Rongbin Li, Xinpeng Ding et al.AAAI 2026
- U2-BENCH: Benchmarking Large Vision-Language Models on Ultrasound UnderstandingAnjie Le, Henan Liu, Yue Wang, Zhenyu Liu et al.ICLR 2026 · 8 citations
- FETAL-GAUGE: A BENCHMARK FOR ASSESSING VISION-LANGUAGE MODELS IN FETAL ULTRASOUNDHussain Alasmawi, Numan Saeed, Mohammad YaqubICLR 2026
