no code implementations • 24 Apr 2024 • Xuxin Chen, Yuheng Li, Mingzhe Hu, Ella Salari, Xiaoqian Chen, Richard L. J. Qiu, Bin Zheng, Xiaofeng Yang
For framework evaluation, we assembled two datasets retrospectively.
no code implementations • 13 Sep 2023 • Ke Zhang, Neman Abdoli, Patrik Gilley, Youkabed Sadri, Xuxin Chen, Theresa C. Thai, Lauren Dockery, Kathleen Moore, Robert S. Mannel, Yuchen Qiu
To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy response prediction of the NACT at an early stage.
no code implementations • 28 Mar 2023 • Neman Abdoli, Ke Zhang, Patrik Gilley, Xuxin Chen, Youkabed Sadri, Theresa C. Thai, Lauren E. Dockery, Kathleen Moore, Robert S. Mannel, Yuchen Qiu
2D and 3D tumor features are widely used in a variety of medical image analysis tasks.
no code implementations • 21 Jun 2022 • Xuxin Chen, Ke Zhang, Neman Abdoli, Patrik W. Gilley, Ximin Wang, Hong Liu, Bin Zheng, Yuchen Qiu
For this purpose, we employ local Transformer blocks to separately learn patch relationships within four mammograms acquired from two-view (CC/MLO) of two-side (right/left) breasts.
no code implementations • 25 Jan 2022 • Xuxin Chen, Ximin Wang, Ke Zhang, Kar-Ming Fung, Theresa C. Thai, Kathleen Moore, Robert S. Mannel, Hong Liu, Bin Zheng, Yuchen Qiu
This study aims to develop a novel computer-aided diagnosis (CAD) scheme for mammographic breast mass classification using semi-supervised learning.
no code implementations • 27 May 2021 • Xuxin Chen, Ximin Wang, Ke Zhang, Kar-Ming Fung, Theresa C. Thai, Kathleen Moore, Robert S. Mannel, Hong Liu, Bin Zheng, Yuchen Qiu
Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis.