1 code implementation • 13 Jul 2022 • Kaiwen Xu, Thomas Li, Mirza S. Khan, Riqiang Gao, Sanja L. Antic, Yuankai Huo, Kim L. Sandler, Fabien Maldonado, Bennett A. Landman
We evaluate the validity of the proposed method in automatic BC assessment using lung screening CT with limited FOV.
no code implementations • 17 Jun 2022 • Riqiang Gao, Thomas Li, Yucheng Tang, Zhoubing Xu, Michael Kammer, Sanja L. Antic, Kim Sandler, Fabien Moldonado, Thomas A. Lasko, Bennett Landman
We believe that this study has merits to guide readers to choose calibration models and understand gaps between general computer vision and medical imaging domains.
no code implementations • 19 Oct 2020 • Riqiang Gao, Yucheng Tang, Kaiwen Xu, Michael N. Kammer, Sanja L. Antic, Steve Deppen, Kim L. Sandler, Pierre P. Massion, Yuankai Huo, Bennett A. Landman
The network can be trained end-to-end with both medical image features and CDEs/biomarkers, or make a prediction with single modality.
1 code implementation • 16 Dec 2019 • Yiyuan Yang, Riqiang Gao, Yucheng Tang, Sanja L. Antic, Steve Deppen, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman
To improve performance on the primary task, we propose an Internal-Transfer Weighting (ITW) strategy to suppress the loss functions on auxiliary tasks for the final stages of training.
no code implementations • 12 Nov 2019 • Riqiang Gao, Lingfeng li, Yucheng Tang, Sanja L. Antic, Alexis B. Paulson, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman
Annual low dose computed tomography (CT) lung screening is currently advised for individuals at high risk of lung cancer (e. g., heavy smokers between 55 and 80 years old).
no code implementations • 11 Sep 2019 • Riqiang Gao, Yuankai Huo, Shunxing Bao, Yucheng Tang, Sanja L. Antic, Emily S. Epstein, Aneri B. Balar, Steve Deppen, Alexis B. Paulson, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman
To model both regular and irregular longitudinal samples, we generalize the LSTM model with the Distanced LSTM (DLSTM) for temporally varied acquisitions.
no code implementations • 21 Feb 2019 • Jiachen Wang, Riqiang Gao, Yuankai Huo, Shunxing Bao, Yunxi Xiong, Sanja L. Antic, Travis J. Osterman, Pierre P. Massion, Bennett A. Landman
The results show that the AUC obtained from clinical demographics alone was 0. 635 while the attention network alone reached an accuracy of 0. 687.