Zobrazeno 1 - 10
of 1 028
pro vyhledávání: '"Yin, Fang‐Fang"'
Autor:
Chen, Yang, Yang, Zhenyu, Zhao, Jingtong, Adamson, Justus, Sheng, Yang, Yin, Fang-Fang, Wang, Chunhao
We developed a deep ensemble learning model with a radiomics spatial encoding execution for improved glioma segmentation accuracy using multi-parametric MRI (mp-MRI). This model was developed using 369 glioma patients with a 4-modality mp-MRI protoco
Externí odkaz:
http://arxiv.org/abs/2303.10533
Autor:
Yang, Zhenyu, Lafata, Kyle, Vaios, Eugene, Hu, Zongsheng, Mullikin, Trey, Yin, Fang-Fang, Wang, Chunhao
The projection of planar MRI data onto a spherical surface is equivalent to a nonlinear image transformation that retains global anatomical information. By incorporating this image transformation process in our proposed spherical projection-based U-N
Externí odkaz:
http://arxiv.org/abs/2210.06512
Autor:
Hu, Zongsheng, Yang, Zhenyu, Zhang, Haozhao, Vaios, Eugene, Lafata, Kyle, Yin, Fang-Fang, Wang, Chunhao
Purpose: To develop a novel deep-learning model that integrates radiomics analysis in a multi-dimensional feature fusion workflow for glioblastoma (GBM) post-resection survival prediction. Methods: A cohort of 235 GBM patients with complete surgical
Externí odkaz:
http://arxiv.org/abs/2203.05891
Autor:
Yang, Zhenyu, Hu, Zongsheng, Ji, Hangjie, Lafata, Kyle, Floyd, Scott, Yin, Fang-Fang, Wang, Chunhao
Purpose: To develop a neural ordinary differential equation (ODE) model for visualizing deep neural network (DNN) behavior during multi-parametric MRI (mp-MRI) based glioma segmentation as a method to enhance deep learning explainability. Methods: By
Externí odkaz:
http://arxiv.org/abs/2203.00628
To develop a deep-learning model that integrates radiomics analysis for enhanced performance of COVID-19 and Non-COVID-19 pneumonia detection using chest X-ray image, two deep-learning models were trained based on a pre-trained VGG-16 architecture: i
Externí odkaz:
http://arxiv.org/abs/2107.08667
Autor:
Yang, Zhenyu, Lafata, Kyle J, Chen, Xinru, Bowsher, James, Chang, Yushi, Wang, Chunhao, Yin, Fang-Fang
Purpose: To develop a radiomics filtering technique for characterizing spatial-encoded regional pulmonary ventilation information on lung CT. Methods: The lung volume was segmented on 46 CT images, and a 3D sliding window kernel was implemented acros
Externí odkaz:
http://arxiv.org/abs/2105.11171
Autor:
Ji, Hangjie, Lafata, Kyle, Mowery, Yvonne, Brizel, David, Bertozzi, Andrea L., Yin, Fang-Fang, Wang, Chunhao
This paper develops a method of biologically guided deep learning for post-radiation FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information. Based on the classic reaction-diffusion mechanism, a novel biologic
Externí odkaz:
http://arxiv.org/abs/2105.10650
Autor:
Li, Xinyi, Sheng, Yang, Zhang, Jiahan, Wang, Wentao, Yin, Fang-Fang, Wu, Qiuwen, Ge, Yaorong, Wu, Q. Jackie, Wang, Chunhao
Purpose: To develop an Artificial Intelligence (AI) agent for fully-automated rapid head and neck (H&N) IMRT plan generation without time-consuming inverse planning.$$$$ Methods: This AI agent was trained using a conditional Generative Adversarial Ne
Externí odkaz:
http://arxiv.org/abs/2009.12898
Autor:
Zhang, Jiahan, Wang, Chunhao, Sheng, Yang, Palta, Manisha, Czito, Brian, Willett, Christopher, Zhang, Jiang, Jensen, P James, Yin, Fang-Fang, Wu, Qiuwen, Ge, Yaorong, Wu, Q Jackie
Pancreas stereotactic body radiotherapy treatment planning requires planners to make sequential, time consuming interactions with the treatment planning system (TPS) to reach the optimal dose distribution. We seek to develop a reinforcement learning
Externí odkaz:
http://arxiv.org/abs/2009.07997
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