Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Adam P. Harrison"'
Autor:
Xianghua Ye, Dazhou Guo, Jia Ge, Senxiang Yan, Yi Xin, Yuchen Song, Yongheng Yan, Bing-shen Huang, Tsung-Min Hung, Zhuotun Zhu, Ling Peng, Yanping Ren, Rui Liu, Gong Zhang, Mengyuan Mao, Xiaohua Chen, Zhongjie Lu, Wenxiang Li, Yuzhen Chen, Lingyun Huang, Jing Xiao, Adam P. Harrison, Le Lu, Chien-Yu Lin, Dakai Jin, Tsung-Ying Ho
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-15 (2022)
Accurate organ at risk (OAR) segmentation is critical to reduce the radiotherapy post-treatment complications. Here, the authors develop an automated OAR segmentation system to delineate a comprehensive set of 42 H&N OARs.
Externí odkaz:
https://doaj.org/article/1ab967b1b083436aa371fefb23939ea9
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:
Adam P. Harrison, Bowen Li, Tse-Hwa Hsu, Cheng-Jen Chen, Wan-Ting Yu, Jennifer Tai, Le Lu, Dar-In Tai
Publikováno v:
Diagnostics, Vol 13, Iss 20, p 3225 (2023)
Introduction: A deep learning algorithm to quantify steatosis from ultrasound images may change a subjective diagnosis to objective quantification. We evaluate this algorithm in patients with weight changes. Materials and Methods: Patients (N = 101)
Externí odkaz:
https://doaj.org/article/77b253a2a8f447dcb2aba2d4ff50d68f
Autor:
Christopher M. Perry, Tarkeshwar Singh, Kayla G. Springer, Adam T. Harrison, Alexander C. McLain, Troy M. Herter
Publikováno v:
Journal of NeuroEngineering and Rehabilitation, Vol 17, Iss 1, Pp 1-19 (2020)
Abstract Background Our ability to acquire, refine and adapt skilled limb movements is a hallmark of human motor learning that allows us to successfully perform many daily activities. The capacity to acquire, refine and adapt other features of motor
Externí odkaz:
https://doaj.org/article/5a28d47c0dd045b38225bf3f9ddec09b
Autor:
Jennifer Tai, Adam P Harrison, Hui-Ming Chen, Chiu-Yi Hsu, Tse-Hwa Hsu, Cheng-Jen Chen, Wen-Juei Jeng, Ming-Ling Chang, Le Lu, Dar-In Tai
Publikováno v:
World Journal of Gastroenterology. 29:2188-2201
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::758c4e3869303d691e50d7ae12024f0e
https://doi.org/10.3748/wjg.v29.i14.2188
https://doi.org/10.3748/wjg.v29.i14.2188
Window Loss for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotation
Autor:
Xinyu Zhang, Yirui Wang, Chi-Tung Cheng, Le Lu, Adam P. Harrison, Jing Xiao, Chien-Hung Liao, Shun Miao
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 35:724-732
Object detection methods are widely adopted for computer-aided diagnosis using medical images. Anomalous findings are usually treated as objects that are described by bounding boxes. Yet, many pathological findings, e.g., bone fractures, cannot be cl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6a8b9044a04a62404286cc0d338c2bad
https://doi.org/10.1609/aaai.v35i1.16153
https://doi.org/10.1609/aaai.v35i1.16153
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
Autor:
Bowen, Li, Dar-In, Tai, Ke, Yan, Yi-Cheng, Chen, Cheng-Jen, Chen, Shiu-Feng, Huang, Tse-Hwa, Hsu, Wan-Ting, Yu, Jing, Xiao, Lu, Le, Adam P, Harrison
Publikováno v:
World journal of gastroenterology. 28(22)
Hepatic steatosis is a major cause of chronic liver disease. Two-dimensional (2D) ultrasound is the most widely used non-invasive tool for screening and monitoring, but associated diagnoses are highly subjective.To develop a scalable deep learning (D
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::94382467e062419304fb7638908904df
https://pubmed.ncbi.nlm.nih.gov/35979264
https://pubmed.ncbi.nlm.nih.gov/35979264
Autor:
Xianghua Ye, Dazhou Guo, Jia Ge, Senxiang Yan, Yi Xin, Yuchen Song, Yongheng Yan, Bing-shen Huang, Tsung-Min Hung, Zhuotun Zhu, Ling Peng, Yanping Ren, Rui Liu, Gong Zhang, Mengyuan Mao, Xiaohua Chen, Zhongjie Lu, Wenxiang Li, Yuzhen Chen, Lingyun Huang, Jing Xiao, Adam P. Harrison, Le Lu, Chien-Yu Lin, Dakai Jin, Tsung-Ying Ho
Publikováno v:
Nature communications. 13(1)
Accurate organ-at-risk (OAR) segmentation is critical to reduce radiotherapy complications. Consensus guidelines recommend delineating over 40 OARs in the head-and-neck (H&N). However, prohibitive labor costs cause most institutions to delineate a su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cb292918ce2cf54c6dffb4ee9b20b001
https://pubmed.ncbi.nlm.nih.gov/36253346
https://pubmed.ncbi.nlm.nih.gov/36253346
Autor:
Yanping Ren, Xiaohua Chen, Tsung-Ying Ho, Xianghua Ye, Tsung-Min Hung, Yi Xin, Yuchen Song, Mengyuan Mao, Zhuotun Zhu, Dazhou Guo, Chien-Yu Lin, Dakai Jin, Wenxiang Li, Zhongjie Lu, Bing-shen Huang, Rui Liu, Le Lu, Adam P. Harrison, Jing Xiao, Ling Peng, Gong Zhang, Jia Ge, Lingyun Huang, Chen Yuzhen, Senxiang Yan
Accurate organ at risk (OAR) segmentation is critical to reduce the radiotherapy post-treatment complications. Consensus guidelines recommend a set of more than 40 OARs in the head and neck (H&N) region, however, due to the predictable prohibitive la
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5540f5f1049ccaa2b7041dec75c66680
http://arxiv.org/abs/2111.01544
http://arxiv.org/abs/2111.01544
