Ultrasound-based dominant intraprostatic lesion classification with swin transformer

Yuheng Li; Boran Zhou; Jing Wang; Shaoyan Pan; Ashesh Jani; Tian Liu; Pretsh Patel; Xiaofeng Yang

doi:10.1117/12.2653634

10 April 2023 Ultrasound-based dominant intraprostatic lesion classification with swin transformer

Yuheng Li, Boran Zhou, Jing Wang, Shaoyan Pan, Ashesh Jani, Tian Liu, Pretsh Patel, Xiaofeng Yang

Proceedings Volume 12470, Medical Imaging 2023: Ultrasonic Imaging and Tomography; 124700T (2023) https://doi.org/10.1117/12.2653634
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

In prostate brachytherapy, focal boost on dominant intraprostatic lesions (DILs) can reduce the recurrence rate while keeping low toxicity. In recent years, ultrasound (US) prostate tissue characterization has demonstrated the feasibility in detecting dominant intraprostatic lesions. With recent developments in computer-aided diagnosis (CAD), deep learningbased methods have provided solutions for efficient analysis of US images. In this study, we aim to develop a Shiftedwindows (Swin) Transformer-based method for DIL classification. The self-attention layers in Swin Transformer allow efficient feature discrimination between benign tissues and intraprostatic lesions. We simplified the structure of Swin Transformer to avoid overfitting on a small dataset. The proposed transformer structure achieved 83% accuracy and 0.86 AUC at patient level on three-fold cross validation, demonstrating the feasibility of applying our method for dominant lesion classification from US images, which is of clinical significance for radiotherapy treatment planning.

Citation Download Citation

Yuheng Li, Boran Zhou, Jing Wang, Shaoyan Pan, Ashesh Jani, Tian Liu, Pretsh Patel, and Xiaofeng Yang "Ultrasound-based dominant intraprostatic lesion classification with swin transformer", Proc. SPIE 12470, Medical Imaging 2023: Ultrasonic Imaging and Tomography, 124700T (10 April 2023); https://doi.org/10.1117/12.2653634

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