Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Lun M, Wong"'
Autor:
Jing Hao, Lun M. Wong, Zhiyi Shan, Qi Yong H. Ai, Xieqi Shi, James Kit Hon Tsoi, Kuo Feng Hung
Publikováno v:
Diagnostics, Vol 14, Iss 17, p 1948 (2024)
Automated tooth segmentation and identification on dental radiographs are crucial steps in establishing digital dental workflows. While deep learning networks have been developed for these tasks, their performance has been inferior in partially edent
Externí odkaz:
https://doaj.org/article/74f36a97765242f3b353d43017b0fbc9
Autor:
Kuo Feng Hung, Qi Yong H. Ai, Lun M. Wong, Andy Wai Kan Yeung, Dion Tik Shun Li, Yiu Yan Leung
Publikováno v:
Diagnostics, Vol 13, Iss 1, p 110 (2022)
The increasing use of computed tomography (CT) and cone beam computed tomography (CBCT) in oral and maxillofacial imaging has driven the development of deep learning and radiomics applications to assist clinicians in early diagnosis, accurate prognos
Externí odkaz:
https://doaj.org/article/f27e84c52bc54bb79f4462f96510649c
Autor:
W K Jacky Lam, Ann D King, Jacob A Miller, Zhiwei Liu, Kelly J Yu, Melvin L K Chua, Brigette B Y Ma, Ming Yuan Chen, Benjamin A Pinsky, Pei-Jen Lou, John K S Woo, Wan-Lun Hsu, Julia Simon, Denise L Doolan, Tim Waterboer, Edwin P Hui, Hui Li, Raymond K Tsang, Kenneth C W Wong, Julian P Goh, Alexander C Vlantis, Qi Yong Ai, Lun M Wong, Victor Abdullah, Jin Ching Lin, Chien-Jen Chen, Ruth M Pfeiffer, Quynh-Thu Le, Anne W M Lee, Mingfang Ji, Sumei Cao, Jun Ma, Anthony T C Chan, K C Allen Chan, Allan Hildesheim
Publikováno v:
JNCI: Journal of the National Cancer Institute. 115:355-364
A meeting of experts was held in November 2021 to review and discuss available data on performance of Epstein-Barr virus (EBV)–based approaches to screen for early stage nasopharyngeal carcinoma (NPC) and methods for the investigation and managemen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e0a0bc675e95e7f426c52bc21174ade5
https://doi.org/10.1093/jnci/djad012
https://doi.org/10.1093/jnci/djad012
Autor:
Lin Shi, Edwin P. Hui, Macy Tong, Lun M. Wong, Brigette B.Y. Ma, Ann D. King, Darren M.C. Poon, Qi-Yong Ai
Publikováno v:
Quant Imaging Med Surg
BACKGROUND: Convolutional neural networks (CNNs) have the potential to automatically delineate primary nasopharyngeal carcinoma (NPC) on magnetic resonance imaging (MRI), but currently, the literature lacks a module to introduce valuable pre-computed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::67d62987fcf35c89eea41cbc12c61a4a
https://doi.org/10.21037/qims-21-196
https://doi.org/10.21037/qims-21-196
Autor:
Rongli Zhang, Qi Yong H. Ai, Lun M. Wong, Christopher Green, Sahrish Qamar, Tiffany Y. So, Alexander C. Vlantis, Ann D. King
Publikováno v:
Cancers; Volume 14; Issue 23; Pages: 5804
The lack of a consistent MRI radiomic signature, partly due to the multitude of initial feature analyses, limits the widespread clinical application of radiomics for the discrimination of salivary gland tumors (SGTs). This study aimed to identify the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff0ae7e142429d2f233deb3a1e360265
https://pubmed.ncbi.nlm.nih.gov/36497285
https://pubmed.ncbi.nlm.nih.gov/36497285
Publikováno v:
Japanese Journal of Radiology. 39:571-579
Convolutional neural networks (CNNs) show potential for delineating cancers on contrast-enhanced MRI (ce-MRI) but there are clinical scenarios in which administration of contrast is not desirable. We investigated performance of the CNN for delineatin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9d7820e3420829af5636b16d00afa877
https://doi.org/10.1007/s11604-021-01092-x
https://doi.org/10.1007/s11604-021-01092-x
Publikováno v:
Cancers; Volume 14; Issue 14; Pages: 3433
Discriminating early-stage nasopharyngeal carcinoma (NPC) from benign hyperplasia (BH) on MRI is a challenging but important task for the early detection of NPC in screening programs. Radiomics models have the potential to meet this challenge, but in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55684fe27a3d3677550ac9d62fa39f9a
https://pubmed.ncbi.nlm.nih.gov/35884494
https://pubmed.ncbi.nlm.nih.gov/35884494
Autor:
Rongli Zhang, Ann D. King, Lun M. Wong, Kunwar S. Bhatia, Sahrish Qamar, Frankie K.F. Mo, Alexander C. Vlantis, Qi Yong H. Ai
Publikováno v:
Diagnostic and interventional imaging.
The purpose of this study was to retrospectively evaluate the diagnostic performances of diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM) for discriminating between benign and malignant salivary gland tumors (SGTs).Sixty-seven
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e64ac3e998d4541e1534c27e085582b9
https://pubmed.ncbi.nlm.nih.gov/36096875
https://pubmed.ncbi.nlm.nih.gov/36096875
Autor:
W K Jacky Lam, Qi-Yong Ai, Darren M.C. Poon, Frankie Mo, K.C. Allen Chan, Ann D. King, Lun M. Wong, Brigette B.Y. Ma
Publikováno v:
European Radiology. 31:3856-3863
A convolutional neural network (CNN) was adapted to automatically detect early-stage nasopharyngeal carcinoma (NPC) and discriminate it from benign hyperplasia on a non-contrast-enhanced MRI sequence for potential use in NPC screening programs. We re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8129cc3a09f341cd101ddf0dc30946cb
https://doi.org/10.1007/s00330-020-07451-y
https://doi.org/10.1007/s00330-020-07451-y
Autor:
Kuo Feng, Hung, Qi Yong H, Ai, Ann D, King, Michael M, Bornstein, Lun M, Wong, Yiu Yan, Leung
Publikováno v:
Clinical oral investigations. 26(5)
To propose and evaluate a convolutional neural network (CNN) algorithm for automatic detection and segmentation of mucosal thickening (MT) and mucosal retention cysts (MRCs) in the maxillary sinus on low-dose and full-dose cone-beam computed tomograp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::f690e2cb075f2b72cef50206ea152b5c
https://pubmed.ncbi.nlm.nih.gov/35032193
https://pubmed.ncbi.nlm.nih.gov/35032193