Ultrasound breast tumor detection based on vision graph neural network

Mingzhe Hu; Jing Wang; Chih-wei Chang; Tian Liu; Xiaofeng Yang

doi:10.1117/12.2654077

10 April 2023 Ultrasound breast tumor detection based on vision graph neural network

Mingzhe Hu, Jing Wang, Chih-wei Chang, Tian Liu, Xiaofeng Yang

Proceedings Volume 12470, Medical Imaging 2023: Ultrasonic Imaging and Tomography; 124700W (2023) https://doi.org/10.1117/12.2654077
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Breast cancer is the most commonly diagnosed cancer in women in the United States. Early detection of breast tumors enables prompt determination of cancer status, significantly boosting patient survival rate. Non-invasive and non-ionizing ultrasound imaging is a widely used diagnosing modality in clinic. To assist clinicians in breast cancer diagnosis, we implemented a vision graph neural networks (ViG)-based pipeline that can achieve accurate binary classification (normal vs. breast tumor) and multiclass classification (normal, benign, and malignant) from breast ultrasound images. Our results demonstrated that the average accuracy of ViG is 100.00% for binary and 87.18% for multiclass classification tasks. To the best of our knowledge, this is the first end-to-end, graph-feature-based deep learning pipeline to achieve accurate breast tumor detection from ultrasound images. The proposed ViG-based classifier is accessible for clinical implementation and has the potential to enhance lesion detection from ultrasound images.

Citation Download Citation

Mingzhe Hu, Jing Wang, Chih-wei Chang, Tian Liu, and Xiaofeng Yang "Ultrasound breast tumor detection based on vision graph neural network", Proc. SPIE 12470, Medical Imaging 2023: Ultrasonic Imaging and Tomography, 124700W (10 April 2023); https://doi.org/10.1117/12.2654077

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available