Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Jinzheng Cai"'
Autor:
Chi‐Tung Cheng, Jinzheng Cai, Wei Teng, Youjing Zheng, Yu‐Ting Huang, Yu‐Chao Wang, Chien‐Wei Peng, Youbao Tang, Wei‐Chen Lee, Ta‐Sen Yeh, Jing Xiao, Le Lu, Chien‐Hung Liao, Adam P. Harrison
Publikováno v:
Hepatology Communications, Vol 6, Iss 10, Pp 2901-2913 (2022)
Abstract Hepatocellular carcinoma (HCC) can be potentially discovered from abdominal computed tomography (CT) studies under varied clinical scenarios (e.g., fully dynamic contrast‐enhanced [DCE] studies, noncontrast [NC] plus venous phase [VP] abdo
Externí odkaz:
https://doaj.org/article/00f466c4081a45a28e1e67b4aacb9ad8
Autor:
Tzu-Chen Yen, Ling Zhang, Le Lu, Jing Xiao, Li-Yu Lee, Chien-Yu Lin, Hung-Ming Wang, Chun-Ta Liao, Yuankai Huo, Isabella Nogues, Wenlan Zhou, Kui Zhao, Shilin Zhao, Xianghua Ye, Jinzheng Cai, Jiawen Yao, Nai-Ming Cheng
Time-dependent ROC analyses, 2-year survival, DeepPET-OPSCC score, clinical factors, nomograms, clinical deployment test cohort
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1d38183947eacfd36d27b78521e66493
https://doi.org/10.1158/1078-0432.22480317.v1
https://doi.org/10.1158/1078-0432.22480317.v1
Autor:
Tzu-Chen Yen, Ling Zhang, Le Lu, Jing Xiao, Li-Yu Lee, Chien-Yu Lin, Hung-Ming Wang, Chun-Ta Liao, Yuankai Huo, Isabella Nogues, Wenlan Zhou, Kui Zhao, Shilin Zhao, Xianghua Ye, Jinzheng Cai, Jiawen Yao, Nai-Ming Cheng
Purpose:Accurate prognostic stratification of patients with oropharyngeal squamous cell carcinoma (OPSCC) is crucial. We developed an objective and robust deep learning–based fully-automated tool called the DeepPET-OPSCC biomarker for predicting ov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::34c4df89df7d8d373370d82b7f789ba0
https://doi.org/10.1158/1078-0432.c.6530412
https://doi.org/10.1158/1078-0432.c.6530412
Autor:
Tzu-Chen Yen, Ling Zhang, Le Lu, Jing Xiao, Li-Yu Lee, Chien-Yu Lin, Hung-Ming Wang, Chun-Ta Liao, Yuankai Huo, Isabella Nogues, Wenlan Zhou, Kui Zhao, Shilin Zhao, Xianghua Ye, Jinzheng Cai, Jiawen Yao, Nai-Ming Cheng
Supplementary Tables
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::195d0d1b33dfee2aea998cc58d2797aa
https://doi.org/10.1158/1078-0432.22480281
https://doi.org/10.1158/1078-0432.22480281
Autor:
Tzu-Chen Yen, Ling Zhang, Le Lu, Jing Xiao, Li-Yu Lee, Chien-Yu Lin, Hung-Ming Wang, Chun-Ta Liao, Yuankai Huo, Isabella Nogues, Wenlan Zhou, Kui Zhao, Shilin Zhao, Xianghua Ye, Jinzheng Cai, Jiawen Yao, Nai-Ming Cheng
Supplementary Protocol
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a8aa2479dddd4762b82b751bc9f3a75
https://doi.org/10.1158/1078-0432.22480284
https://doi.org/10.1158/1078-0432.22480284
Autor:
Adam P. Harrison, Jing Xiao, Youjing Zheng, Yuankai Huo, Ke Yan, Lin Yang, Jinzheng Cai, Le Lu
Publikováno v:
IEEE Transactions on Medical Imaging. 40:59-70
Acquiring large-scale medical image data, necessary for training machine learning algorithms, is frequently intractable, due to prohibitive expert-driven annotation costs. Recent datasets extracted from hospital archives, e.g., DeepLesion, have begun
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92b9dbb309ebb2f3a37b59d8df0e2fb5
https://doi.org/10.1109/tmi.2020.3022034
https://doi.org/10.1109/tmi.2020.3022034
Publikováno v:
CVPR
Monitoring treatment response in longitudinal studies plays an important role in clinical practice. Accurately identifying lesions across serial imaging follow-up is the core to the monitoring procedure. Typically this incorporates both image and ana
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::922d597a303033c01e2e2a6c7d260a25
https://doi.org/10.1109/cvpr46437.2021.01491
https://doi.org/10.1109/cvpr46437.2021.01491
Autor:
Farah Khalil, Nazeel Ahmad, Hai Su, Pingjun Chen, Jasreman Dhillon, Chunbao Wang, Lei Cui, Yuanpu Xie, Jinzheng Cai, Lin Yang, Xiaoshuang Shi, Fujun Liu, Zizhao Zhang, Mason McGough, Marilyn M. Bui, Fuyong Xing, Shohreh I. Dickinson, Manish Sapkota
Publikováno v:
Nature Machine Intelligence. 1:236-245
Diagnostic pathology is the foundation and gold standard for identifying carcinomas. However, high inter-observer variability substantially affects productivity in routine pathology and is especially ubiquitous in diagnostician-deficient medical cent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d9099872483e305cca88c9f9883f43a
https://doi.org/10.1038/s42256-019-0052-1
https://doi.org/10.1038/s42256-019-0052-1
Texture analysis for muscular dystrophy classification in MRI with improved class activation mapping
Autor:
Glenn A. Walter, Jinzheng Cai, Krista Vandenborne, Fujun Liu, Abhinandan Batra, Fuyong Xing, Lin Yang
Publikováno v:
Pattern Recognition. 86:368-375
The muscular dystrophies are made up of a diverse group of rare genetic diseases characterized by progressive loss of muscle strength and muscle damage. Since there is no cure for muscular dystrophy and clinical outcome measures are limited, it is cr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad3ab2d625069e461377bf9b0d538de7
https://doi.org/10.1016/j.patcog.2018.08.012
https://doi.org/10.1016/j.patcog.2018.08.012
Publikováno v:
Artificial Intelligence in Medicine ISBN: 9780128212592
The interest in artificial intelligence (AI) has ballooned within radiology in the past few years primarily due to notable successes of deep learning. With the advances brought by deep learning, AI has the potential to recognize and localize complex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f8d8576856e66c533ef035095aab538d
https://doi.org/10.1016/b978-0-12-821259-2.00014-4
https://doi.org/10.1016/b978-0-12-821259-2.00014-4