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Bingzhong Jing

Showing results (1-10 of 16) with videos related to

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IEEE Journal of Biomedical and Health Informatics|March 3, 2025
TGAP-Net: Twin Graph Attention Pseudo-Label Generation for Weakly Supervised Semantic SegmentationHaohua Chen, Yishu Deng, Zhensheng Hu, et al.
Life (Basel, Switzerland)|March 29, 2023
BézierSeg: Parametric Shape Representation for Fast Object Segmentation in Medical ImagesHaichou Chen, Yishu Deng, Bin Li, et al.
Computer Methods and Programs in Biomedicine|March 1, 2022
The contrast-enhanced MRI can be substituted by unenhanced MRI in identifying and automatically segmenting primary nasopharyngeal carcinoma with the aid of deep learning models: An exploratory study in large-scale population of endemic areaYishu Deng, Chaofeng Li, Xing Lv, et al.
Diagnostics (Basel, Switzerland)|January 28, 2026
Automated Tumor and Node Staging from Esophageal Cancer Endoscopic Ultrasound Reports: A Benchmark of Advanced Reasoning Models with Prompt Engineering and Cross-Lingual EvaluationXudong Hu, Lingde Feng, Bingzhong Jing, et al.
Oral Oncology|July 3, 2020
Development of a self-constrained 3D DenseNet model in automatic detection and segmentation of nasopharyngeal carcinoma using magnetic resonance imagesLiangru Ke, Yishu Deng, Weixiong Xia, et al.
Computer Methods and Programs in Biomedicine|August 12, 2020
Deep learning for risk prediction in patients with nasopharyngeal carcinoma using multi-parametric MRIsBingzhong Jing, Yishu Deng, Tao Zhang, et al.
Artificial Intelligence in Medicine|September 16, 2019
A deep survival analysis method based on rankingBingzhong Jing, Tao Zhang, Zixian Wang, et al.
Cancer Imaging : the Official Publication of the International Cancer Imaging Society|March 25, 2025
Establishment of a deep-learning-assisted recurrent nasopharyngeal carcinoma detecting simultaneous tactic (DARNDEST) with high cost-effectiveness based on magnetic resonance images: a multicenter study in an endemic areaYishu Deng, Yingying Huang, Haijun Wu, et al.
European Journal of Radiology|September 18, 2023
Deep learning-based recurrence detector on magnetic resonance scans in nasopharyngeal carcinoma: A multicenter studyYishu Deng, Yingying Huang, Bingzhong Jing, et al.
Cell Reports. Medicine|October 16, 2024
Deep learning model with pathological knowledge for detection of colorectal neuroendocrine tumorKe Zheng, Jinling Duan, Ruixuan Wang, et al.
Pageof 2

Showing results (1-10 of 16) with videos related to

Sort By:
Pageof 2
IEEE Journal of Biomedical and Health Informatics|March 3, 2025
TGAP-Net: Twin Graph Attention Pseudo-Label Generation for Weakly Supervised Semantic SegmentationHaohua Chen, Yishu Deng, Zhensheng Hu, et al.
Life (Basel, Switzerland)|March 29, 2023
BézierSeg: Parametric Shape Representation for Fast Object Segmentation in Medical ImagesHaichou Chen, Yishu Deng, Bin Li, et al.
Computer Methods and Programs in Biomedicine|March 1, 2022
The contrast-enhanced MRI can be substituted by unenhanced MRI in identifying and automatically segmenting primary nasopharyngeal carcinoma with the aid of deep learning models: An exploratory study in large-scale population of endemic areaYishu Deng, Chaofeng Li, Xing Lv, et al.
Diagnostics (Basel, Switzerland)|January 28, 2026
Automated Tumor and Node Staging from Esophageal Cancer Endoscopic Ultrasound Reports: A Benchmark of Advanced Reasoning Models with Prompt Engineering and Cross-Lingual EvaluationXudong Hu, Lingde Feng, Bingzhong Jing, et al.
Oral Oncology|July 3, 2020
Development of a self-constrained 3D DenseNet model in automatic detection and segmentation of nasopharyngeal carcinoma using magnetic resonance imagesLiangru Ke, Yishu Deng, Weixiong Xia, et al.
Computer Methods and Programs in Biomedicine|August 12, 2020
Deep learning for risk prediction in patients with nasopharyngeal carcinoma using multi-parametric MRIsBingzhong Jing, Yishu Deng, Tao Zhang, et al.
Artificial Intelligence in Medicine|September 16, 2019
A deep survival analysis method based on rankingBingzhong Jing, Tao Zhang, Zixian Wang, et al.
Cancer Imaging : the Official Publication of the International Cancer Imaging Society|March 25, 2025
Establishment of a deep-learning-assisted recurrent nasopharyngeal carcinoma detecting simultaneous tactic (DARNDEST) with high cost-effectiveness based on magnetic resonance images: a multicenter study in an endemic areaYishu Deng, Yingying Huang, Haijun Wu, et al.
European Journal of Radiology|September 18, 2023
Deep learning-based recurrence detector on magnetic resonance scans in nasopharyngeal carcinoma: A multicenter studyYishu Deng, Yingying Huang, Bingzhong Jing, et al.
Cell Reports. Medicine|October 16, 2024
Deep learning model with pathological knowledge for detection of colorectal neuroendocrine tumorKe Zheng, Jinling Duan, Ruixuan Wang, et al.
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