MRI-based prostate cancer detection using cross-shaped windows transformer

Yuheng Li; Jacob Wynne; Jing Wang; Richard L. J. Qiu; Justin Roper; Shaoyan Pan; Ashesh B. Jani; Tian Liu; Pretesh R. Patel; Hui Mao; Xiaofeng Yang

doi:10.1117/12.3006812

3 April 2024 MRI-based prostate cancer detection using cross-shaped windows transformer

Yuheng Li, Jacob Wynne, Jing Wang, Richard L. J. Qiu, Justin Roper, Shaoyan Pan, Ashesh B. Jani, Tian Liu, Pretesh R. Patel, Hui Mao, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129271T (2024) https://doi.org/10.1117/12.3006812
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Prostate multiparametric magnetic resonance imaging (mpMRI) has demonstrated promising results in prostate cancer (PCa) detection using deep learning models using convolutional neural networks (CNNs). Recently, transformers have achieved competitive performance compared to CNNs in computer vision. Large-scale transformers benefit from training with large-scale annotated data, which are expensive and labor-intensive to obtain in medical imaging. Self-supervised learning can effectively leverage unlabeled data to extract useful semantic representations with no additional annotation cost. This can improve model performance on downstream tasks with limited labelled data and increase model robustness to external data. We present a novel end-to-end cross-shaped transformer model CSwin UNet to detect clinically significant prostate cancer (csPCa) in prostate bi-parametric MR imaging (bpMRI). Using a large prostate bpMRI dataset with 1500 patients, our Cswin UNet achieves 0.880±0.013 AUC and 0.790 ±0.033 pAUC, significantly outperforming state-of-the-art CNN and transformer models.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Yuheng Li, Jacob Wynne, Jing Wang, Richard L. J. Qiu, Justin Roper, Shaoyan Pan, Ashesh B. Jani, Tian Liu, Pretesh R. Patel, Hui Mao, and Xiaofeng Yang "MRI-based prostate cancer detection using cross-shaped windows transformer", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129271T (3 April 2024); https://doi.org/10.1117/12.3006812

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