EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal
  Ultrasound Intelligence
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.
68ccaff53df9ac65e93dc5ab
Chaoyin She
claimed_verified
2025-09-19T06:49:12.488Z
6576dc779091da7dc69a0acd
/avatars/dd825a1308e687b23a349ccb64f9a91a.svg
chaoyinshe
user
chaoyinshe
68ccaff53df9ac65e93dc5ac
Ruifang Lu
admin_assigned
2025-09-19T10:27:38.830Z
673c011c79b5a48d3749bbc0
/avatars/8ef91fcf988bc64289c67f859a931658.svg
Rui-Fang Lu
user
Ruimed
68ccaff53df9ac65e93dc5ad
Lida Chen
admin_assigned
2025-09-19T10:27:47.453Z
67d2e10c3b1df5eb08e2fbfc
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/WG_8c5MUJFptouBODBl2h.png
lidachen
user
lidachen
68ccaff53df9ac65e93dc5ae
Wei Wang
admin_assigned
2025-09-19T10:28:07.479Z
62fa0ffe0697d224219a0cb7
/avatars/f0ef59e1c0cf4ab4fe5cee08d488bd03.svg
Wei Wang
user
WeiWang
68ccaff53df9ac65e93dc5af
Qinghua Huang
EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal
  Ultrasound Intelligence
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.
EchoVLM, a vision-language model with a Mixture of Experts architecture, improves ultrasound report generation and diagnosis by leveraging data from multiple anatomical regions.
vision-language models Mixture of Experts ultrasound report generation diagnosis visual question-answering BLEU-1 scores ROUGE-1 scores