EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal
  Ultrasound Intelligence
Authors: name: Chaoyin She, name: Ruifang Lu, name: Lida Chen, name: Wei Wang, name: Qinghua Huang

Ultrasound imaging has become the preferred imaging modality for early cancer
screening due to its advantages of non-ionizing radiation, low cost, and
real-time imaging capabilities. However, conventional ultrasound diagnosis
heavily relies on physician expertise, presenting challenges of high
subjectivity and low diagnostic efficiency. Vision-language models (VLMs) offer
promising solutions for this issue, but existing general-purpose models
demonstrate limited knowledge in ultrasound medical tasks, with poor
generalization in multi-organ lesion recognition and low efficiency across
multi-task diagnostics. To address these limitations, we propose EchoVLM, a
vision-language model specifically designed for ultrasound medical imaging. The
model employs a Mixture of Experts (MoE) architecture trained on data spanning
seven anatomical regions. This design enables the model to perform multiple
tasks, including ultrasound report generation, diagnosis and visual
question-answering (VQA). The experimental results demonstrated that EchoVLM
achieved significant improvements of 10.15 and 4.77 points in BLEU-1 scores and
ROUGE-1 scores respectively compared to Qwen2-VL on the ultrasound report
generation task. These findings suggest that EchoVLM has substantial potential
to enhance diagnostic accuracy in ultrasound imaging, thereby providing a
viable technical solution for future clinical applications. Source code and
model weights are available at https://github.com/Asunatan/EchoVLM.