1 code implementation • 31 May 2023 • Shaoyan Pan, Elham Abouei, Jacob Wynne, Tonghe Wang, Richard L. J. Qiu, Yuheng Li, Chih-Wei Chang, Junbo Peng, Justin Roper, Pretesh Patel, David S. Yu, Hui Mao, Xiaofeng Yang
The proposed model consists of two processes: a forward process which adds Gaussian noise to real CT scans, and a reverse process in which a shifted-window transformer V-net (Swin-Vnet) denoises the noisy CT scans conditioned on the MRI from the same patient to produce noise-free CT scans.
no code implementations • 30 Apr 2023 • 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
We introduce a novel end-to-end Cross-Shaped windows (CSwin) transformer UNet model, CSwin UNet, to detect clinically significant prostate cancer (csPCa) in prostate bi-parametric MR imaging (bpMRI) and demonstrate the effectiveness of our proposed self-supervised pre-training framework.
no code implementations • 25 Feb 2023 • Shaoyan Pan, Shao-Yuan Lo, Min Huang, Chaoqiong Ma, Jacob Wynne, Tonghe Wang, Tian Liu, Xiaofeng Yang
In this work, we propose an adversarial attack-based data augmentation method to improve the deep-learning-based segmentation algorithm for the delineation of Organs-At-Risk (OAR) in abdominal Computed Tomography (CT) to facilitate radiation therapy.
no code implementations • 25 Mar 2021 • Mingquan Lin, Jacob Wynne, Yang Lei, Tonghe Wang, Walter J. Curran, Tian Liu, Xiaofeng Yang
We summarize the latest AI-based methods for tumor subregion analysis and their applications.