Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Hoo-Chang Shin"'
Autor:
Xi Yang, Aokun Chen, Nima PourNejatian, Hoo Chang Shin, Kaleb E. Smith, Christopher Parisien, Colin Compas, Cheryl Martin, Anthony B. Costa, Mona G. Flores, Ying Zhang, Tanja Magoc, Christopher A. Harle, Gloria Lipori, Duane A. Mitchell, William R. Hogan, Elizabeth A. Shenkman, Jiang Bian, Yonghui Wu
Publikováno v:
npj Digital Medicine, Vol 5, Iss 1, Pp 1-9 (2022)
Abstract There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for m
Externí odkaz:
https://doaj.org/article/557f807a3c23460bb1a9275a8e73d09f
Autor:
Robert Leaman, Rezarta Islamaj, Virginia Adams, Mohammed A Alliheedi, João Rafael Almeida, Rui Antunes, Robert Bevan, Yung-Chun Chang, Arslan Erdengasileng, Matthew Hodgskiss, Ryuki Ida, Hyunjae Kim, Keqiao Li, Robert E Mercer, Lukrécia Mertová, Ghadeer Mobasher, Hoo-Chang Shin, Mujeen Sung, Tomoki Tsujimura, Wen-Chao Yeh, Zhiyong Lu
Publikováno v:
Database (Oxford)
The BioCreative National Library of Medicine (NLM)-Chem track calls for a community effort to fine-tune automated recognition of chemical names in the biomedical literature. Chemicals are one of the most searched biomedical entities in PubMed, and—
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5838ceca62b71cd42bdaabd33e73716c
https://europepmc.org/articles/PMC9991492/
https://europepmc.org/articles/PMC9991492/
Autor:
Xi Yang, Nima PourNejatian, Hoo Chang Shin, Kaleb E Smith, Christopher Parisien, Colin Compas, Cheryl Martin, Mona G Flores, Ying Zhang, Tanja Magoc, Christopher A Harle, Gloria Lipori, Duane A Mitchell, William R Hogan, Elizabeth A Shenkman, Jiang Bian, Yonghui Wu
ObjectiveTo develop a large pretrained clinical language model from scratch using transformer architecture; systematically examine how transformer models of different sizes could help 5 clinical natural language processing (NLP) tasks at different li
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6e21c7a494fb67eea13151388203d5b4
https://doi.org/10.1101/2022.02.27.22271257
https://doi.org/10.1101/2022.02.27.22271257
Publikováno v:
Journal of the American College of Radiology
With the advent of artificial intelligence (AI) across many fields and subspecialties, there are considerable expectations for transformative impact. However, there are also concerns regarding the potential abuse of AI. Many scientists have been worr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e89cd7f0adec99a795985f1fad925c3b
https://resolver.caltech.edu/CaltechAUTHORS:20200420-154710784
https://resolver.caltech.edu/CaltechAUTHORS:20200420-154710784
Autor:
Raghav Mani, Evelina Bakhturina, Mohammad Shoeybi, Raul Puri, Hoo-Chang Shin, Mostofa Patwary, Yang Zhang
Publikováno v:
EMNLP (1)
There has been an influx of biomedical domain-specific language models, showing language models pre-trained on biomedical text perform better on biomedical domain benchmarks than those trained on general domain text corpora such as Wikipedia and Book
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a513477f59bbb8a3f2e9946a120ddf8e
Autor:
Swetha Mandava, Ziyue Xu, Jiook Cha, Christopher Forster, Alzheimer’s Disease Neuroimaging Initiative, Sharath Turuvekere Sreenivas, Hoo-Chang Shin, Alvin Ihsani
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597122
MICCAI (2)
MICCAI (2)
Positron Emission Tomography (PET) is now regarded as the gold standard for the diagnosis of Alzheimer’s Disease (AD). However, PET imaging can be prohibitive in terms of cost and planning, and is also among the imaging techniques with the highest
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e8e1a7caa619541781e62878003c3ad5
https://doi.org/10.1007/978-3-030-59713-9_66
https://doi.org/10.1007/978-3-030-59713-9_66
Autor:
Daguang Xu, Xiaosong Wang, Ziyue Xu, Ling Zhang, Hoo-Chang Shin, Holger R. Roth, Dong Yang, Fausto Milletari
Publikováno v:
Simulation and Synthesis in Medical Imaging ISBN: 9783030327774
SASHIMI@MICCAI
SASHIMI@MICCAI
Synthetic CT image with artificially generated lung nodules has been shown to be useful as an augmentation method for certain tasks such as lung segmentation and nodule classification. Most conventional methods are designed as “inpainting” tasks
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::251e2cb7f5d0edfdb5089d2c75a59f22
https://doi.org/10.1007/978-3-030-32778-1_7
https://doi.org/10.1007/978-3-030-32778-1_7
Autor:
Daniel J. Mollura, Hoo-Chang Shin, Ronald M. Summers, Isabella Nogues, Jianhua Yao, Ziyue Xu, Mingchen Gao, Le Lu, Holger R. Roth
Publikováno v:
IEEE Transactions on Medical Imaging. 35:1285-1298
Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and the revival of deep CNN. CNNs enable learning data-driven, highly representative, layered hierarchical image features from
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9969ed07d70e717fa84f60ddcced8444
https://doi.org/10.1109/tmi.2016.2528162
https://doi.org/10.1109/tmi.2016.2528162
Autor:
Ulas Bagci, Le Lu, Adrien Depeursinge, Aaron Wu, Mingchen Gao, Holger R. Roth, Daniel J. Mollura, Hoo-Chang Shin, Ziyue Xu, Georgios Z. Papadakis, Mario Buty, Ronald M. Summers
Publikováno v:
Computer methods in biomechanics and biomedical engineering. Imagingvisualization. 6(1)
Interstitial lung diseases (ILD) involve several abnormal imaging patterns observed in computed tomography (CT) images. Accurate classification of these patterns plays a significant role in precise clinical decision making of the extent and nature of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::112499b243b3e77ee9b51f73ffe6c56f
https://pubmed.ncbi.nlm.nih.gov/29623248
https://pubmed.ncbi.nlm.nih.gov/29623248
Autor:
Jameson K. Rogers, Katherine P. Andriole, Neil A. Tenenholtz, Hoo-Chang Shin, Jeffrey L. Gunter, Mark Michalski, Matthew L. Senjem, Christopher G. Schwarz
Publikováno v:
Simulation and Synthesis in Medical Imaging ISBN: 9783030005351
SASHIMI@MICCAI
SASHIMI@MICCAI
Data diversity is critical to success when training deep learning models. Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models. In this wo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::add41291545adc1daf16802bf630b5ab
https://doi.org/10.1007/978-3-030-00536-8_1
https://doi.org/10.1007/978-3-030-00536-8_1