Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Minjae Woo"'
Autor:
InChan Hwang, Hari Trivedi, Beatrice Brown-Mulry, Linglin Zhang, Vineela Nalla, Aimilia Gastounioti, Judy Gichoya, Laleh Seyyed-Kalantari, Imon Banerjee, MinJae Woo
Publikováno v:
Frontiers in Radiology, Vol 3 (2023)
IntroductionTo date, most mammography-related AI models have been trained using either film or digital mammogram datasets with little overlap. We investigated whether or not combining film and digital mammography during training will help or hinder m
Externí odkaz:
https://doaj.org/article/510870a7a9b24600b756cf8c8b7885f4
Publikováno v:
Cancer Imaging, Vol 21, Iss 1, Pp 1-11 (2021)
Abstract Background Performing Response Evaluation Criteria in Solid Tumor (RECISTS) measurement is a non-trivial task requiring much expertise and time. A deep learning-based algorithm has the potential to assist with rapid and consistent lesion mea
Externí odkaz:
https://doaj.org/article/e2136e4e79a045bcb7c34d9761531626
Autor:
MinJae Woo, Prabodh Mishra, Ju Lin, Snigdhaswin Kar, Nicholas Deas, Caleb Linduff, Sufeng Niu, Yuzhe Yang, Jerome McClendon, D Hudson Smith, Stephen L Shelton, Christopher E Gainey, William C Gerard, Melissa C Smith, Sarah F Griffin, Ronald W Gimbel, Kuang-Ching Wang
Publikováno v:
JMIR mHealth and uHealth, Vol 9, Iss 10, p e32301 (2021)
BackgroundPrehospitalization documentation is a challenging task and prone to loss of information, as paramedics operate under disruptive environments requiring their constant attention to the patients. ObjectiveThe aim of this study is to develop a
Externí odkaz:
https://doaj.org/article/91c6c09a9bee459c8bd9bde0583cdbf9
Publikováno v:
BMJ Open, Vol 10, Iss 11 (2020)
Background A growing number of research studies have reported inter-observer variability in sizes of tumours measured from CT scans. It remains unclear whether the conventional statistical measures correctly evaluate the CT measurement consistency fo
Externí odkaz:
https://doaj.org/article/6b00da514dfa4edd8504ad975b74175e
Autor:
Jiwoong J. Jeong, Brianna L. Vey, Ananth Bhimireddy, Thomas Kim, Thiago Santos, Ramon Correa, Raman Dutt, Marina Mosunjac, Gabriela Oprea-Ilies, Geoffrey Smith, Minjae Woo, Christopher R. McAdams, Mary S. Newell, Imon Banerjee, Judy Gichoya, Hari Trivedi
Publikováno v:
Radiol Artif Intell
Supplemental material is available for this article. Keywords: Mammography, Breast, Machine Learning © RSNA, 2023
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a647aa4369c393135e57de0739c4f41e
https://europepmc.org/articles/PMC9885379/
https://europepmc.org/articles/PMC9885379/
Publikováno v:
Current Problems in Diagnostic Radiology. 50:321-327
Purpose While a growing number of research studies have reported the inter-observer variability in computed tomographic (CT) measurements, there are very few interventional studies performed. We aimed to assess whether a peer benchmarking interventio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f48692f1741fe7c0a553cd3ff2adf187
https://doi.org/10.1067/j.cpradiol.2020.01.008
https://doi.org/10.1067/j.cpradiol.2020.01.008
Autor:
MinJae Woo, Prabodh Mishra, Ju Lin, Snigdhaswin Kar, Nicholas Deas, Caleb Linduff, Sufeng Niu, Yuzhe Yang, Jerome McClendon, D Hudson Smith, Stephen L Shelton, Christopher E Gainey, William C Gerard, Melissa C Smith, Sarah F Griffin, Ronald W Gimbel, Kuang-Ching Wang
BACKGROUND Prehospitalization documentation is a challenging task and prone to loss of information, as paramedics operate under disruptive environments requiring their constant attention to the patients. OBJECTIVE The aim of this study is to develop
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e6e8ee2a2c02c47a80e1a9ed130b55e6
https://doi.org/10.2196/preprints.32301
https://doi.org/10.2196/preprints.32301
Autor:
Sufeng Niu, Ju Lin, Melissa C. Smith, Kuang-Ching Wang, Caleb Linduff, Nicholas Deas, MinJae Woo, Prabodh Mishra, Ronald W. Gimbel, Yuzhe Yang, D. Hudson Smith, Jerome McClendon, Snigdhaswin Kar
Publikováno v:
IJCNN
Operational medical environments require reliable hands-free solutions to extract data from audio captured under noisy scenarios during rescue missions and provide timely information. However, approaches using automatic speech recognition (ASR) and n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::850b08f837f71bfff8c7865ff9d853b9
https://doi.org/10.1109/ijcnn52387.2021.9533607
https://doi.org/10.1109/ijcnn52387.2021.9533607
Publikováno v:
Cancer Imaging, Vol 21, Iss 1, Pp 1-11 (2021)
Cancer Imaging
Cancer Imaging
BackgroundPerforming Response Evaluation Criteria in Solid Tumor (RECISTS) measurement is a non-trivial task requiring much expertise and time. A deep learning-based algorithm has the potential to assist with rapid and consistent lesion measurement.P
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c84d709fcc40615b2cc96456a34d69a
https://doi.org/10.1186/s40644-021-00413-7
https://doi.org/10.1186/s40644-021-00413-7
Publikováno v:
BMJ Open, Vol 10, Iss 11 (2020)
BMJ Open
BMJ Open
BackgroundA growing number of research studies have reported inter-observer variability in sizes of tumours measured from CT scans. It remains unclear whether the conventional statistical measures correctly evaluate the CT measurement consistency for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c458804a338582d403da3b08c42d4e4e
https://bmjopen.bmj.com/content/10/11/e040096.full
https://bmjopen.bmj.com/content/10/11/e040096.full