Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Nazanin, Esmaili"'
Autor:
Cyril H. M. Tang, Jarrel C. Y. Seah, Hassan K. Ahmad, Michael R. Milne, Jeffrey B. Wardman, Quinlan D. Buchlak, Nazanin Esmaili, John F. Lambert, Catherine M. Jones
Publikováno v:
Diagnostics, Vol 13, Iss 14, p 2317 (2023)
This retrospective case-control study evaluated the diagnostic performance of a commercially available chest radiography deep convolutional neural network (DCNN) in identifying the presence and position of central venous catheters, enteric tubes, and
Externí odkaz:
https://doaj.org/article/6b6e11e1b236408f842596df49775bee
Publikováno v:
IEEE Access, Vol 9, Pp 1313-1320 (2021)
Topic modeling is an important application of natural language processing (NLP) that can automatically identify the set of main topics of a given, typically large, collection of documents. In addition to identifying the main topics in the given colle
Externí odkaz:
https://doaj.org/article/07c540ca07b1430ea3c45cd478baaf1a
Autor:
Quinlan D. Buchlak, Nazanin Esmaili, Christine Bennett, Yi Yuen Wang, James King, Tony Goldschlager
Publikováno v:
PLoS ONE, Vol 17, Iss 7 (2022)
Background Patients with pituitary lesions experience decrements in quality of life (QoL) and treatment aims to arrest or improve QoL decline. Objective To detect associations with QoL in trans-nasal endoscopic skull base surgery patients and train s
Externí odkaz:
https://doaj.org/article/fe7174b426344648a2fbf0b6f3c9e3d0
Autor:
Hassan K. Ahmad, Michael R. Milne, Quinlan D. Buchlak, Nalan Ektas, Georgina Sanderson, Hadi Chamtie, Sajith Karunasena, Jason Chiang, Xavier Holt, Cyril H. M. Tang, Jarrel C. Y. Seah, Georgina Bottrell, Nazanin Esmaili, Peter Brotchie, Catherine Jones
Publikováno v:
Diagnostics, Vol 13, Iss 4, p 743 (2023)
Limitations of the chest X-ray (CXR) have resulted in attempts to create machine learning systems to assist clinicians and improve interpretation accuracy. An understanding of the capabilities and limitations of modern machine learning systems is nec
Externí odkaz:
https://doaj.org/article/571a11ab740e4109a505dc682f9f2a14
Autor:
Jones, Cyril H. M. Tang, Jarrel C. Y. Seah, Hassan K. Ahmad, Michael R. Milne, Jeffrey B. Wardman, Quinlan D. Buchlak, Nazanin Esmaili, John F. Lambert, Catherine M.
Publikováno v:
Diagnostics; Volume 13; Issue 14; Pages: 2317
This retrospective case-control study evaluated the diagnostic performance of a commercially available chest radiography deep convolutional neural network (DCNN) in identifying the presence and position of central venous catheters, enteric tubes, and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::a76b7bc52a5005727b491c011cec1e42
Publikováno v:
PLoS ONE, Vol 14, Iss 4, p e0214973 (2019)
[This corrects the article DOI: 10.1371/journal.pone.0206274.].
Externí odkaz:
https://doaj.org/article/5e452c7df9d54ee7a7d69d9f44d41d1a
Publikováno v:
PLoS ONE, Vol 13, Iss 11, p e0206274 (2018)
BACKGROUND:Transport injuries commonly result in significant disease burden, leading to physical disability, mental health deterioration and reduced quality of life. Analyzing the patterns of healthcare service utilization after transport injuries ca
Externí odkaz:
https://doaj.org/article/15f7b6c5f4354c2183e7a524f3424323
Topic modelling is an important approach of unsupervised machine learning that allows automatically extracting the main “topics” from large collections of documents. In addition, topic modelling is able to identify the topic proportions of each i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::82a376bab1ca4b7d26cd91558a5099bd
https://hdl.handle.net/10453/170440
https://hdl.handle.net/10453/170440
Autor:
Catherine M Jones, Quinlan D. Buchlak, Nazanin Esmaili, Ben Hachey, John F Lambert, Luke Oakden-Rayner, Hassan Ahmad, Anuar Aimoldin, Stephen J F Hogg, Cyril H M Tang, Xavier G Holt, Peter Brotchie, Hung N. Pham, Christine Bennett, Jarrel Seah, Jeffrey B Wardman, Benjamin P Johnston
Publikováno v:
The Lancet Digital Health. 3:e496-e506
Summary Background Chest x-rays are widely used in clinical practice; however, interpretation can be hindered by human error and a lack of experienced thoracic radiologists. Deep learning has the potential to improve the accuracy of chest x-ray inter
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9cf7ee0484144cc6c6a54d4555354ccd
https://doi.org/10.1016/s2589-7500(21)00106-0
https://doi.org/10.1016/s2589-7500(21)00106-0