Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Michael Kawczynski"'
Publikováno v:
Multimodal Biomedical Imaging XVI.
Geographic atrophy (GA) is late-stage dry age-related macular degeneration (AMD). Improved predictors of GA progression would be useful in clinical trial design and may be relevant for clinical practice. The purpose of this study was to accurately pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::03baf5aa4abbcb70f43ba7fa242da0e8
https://doi.org/10.1117/12.2575898
https://doi.org/10.1117/12.2575898
Autor:
Dexter Hadley, Maryam Panahiazar, Dmytro Lituiev, Hari Trivedi, Youngho Seo, Michael Kawczynski, Benjamin L. Franc, Roy Harnish, Beau Norgeot
Publikováno v:
Journal of digital imaging, vol 32, iss 2
Journal of Digital Imaging
Journal of Digital Imaging
Applying state-of-the-art machine learning techniques to medical images requires a thorough selection and normalization of input data. One of such steps in digital mammography screening for breast cancer is the labeling and removal of special diagnos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::335d7ed967310cc95b2b7c632fde50df
https://doi.org/10.1007/s10278-018-0154-z
https://doi.org/10.1007/s10278-018-0154-z
Autor:
Thomas Bengtsson, Jian Dai, Simon S. Gao, Jeffrey R. Willis, J Jill Hopkins, Michael Kawczynski
Publikováno v:
Translational Vision Science & Technology
Purpose To develop deep learning (DL) models to predict best-corrected visual acuity (BCVA) from optical coherence tomography (OCT) images from patients with neovascular age-related macular degeneration (nAMD). Methods Retrospective analysis of OCT i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2188a01ce88493ad70f91103ef764589
https://pubmed.ncbi.nlm.nih.gov/32974088
https://pubmed.ncbi.nlm.nih.gov/32974088
Autor:
Hyo-Eun Kim, Jiashi Feng, Stephen H. Friend, Ljubomir Buturovic, Dezső Ribli, Luis Caballero, Li Shen, Fredrik Strand, Yaroslav Nikulin, Krzysztof J. Geras, Kyunghyun Cho, Elias Chaibub Neto, Rami Ben-Ari, Christoph I. Lee, Zequn Jie, Imane Nedjar, Felix Nensa, Darvin Yi, Shivanthan A.C. Yohanandan, Bruce Hoff, Justin Guinney, Jaime S. Cardoso, Russell B. McBride, Mengling Feng, Yiqiu Shen, Simona Rabinovici-Cohen, Ethan Goan, Stefan Harrer, Sven Koitka, Michael Kawczynski, Hari Trivedi, Karl Trygve Kalleberg, Christoph M. Friedrich, F. Albiol, Dimitri Perrin, Jose Costa Pereira, Umar Asif, Bibo Shi, Zbigniew Wojna, Antonio Jimeno Yepes, Peter Lindholm, Berkman Sahiner, Sijia Wang, Thea Norman, Weiva Sieh, Joyce Cahoon, Gerard Cardoso Negrie, Pavitra Krishnaswamy, Diana S. M. Buist, Alberto Albiol, Lester Mackey, Hwejin Jung, Laurie R. Margolies, Gaurav Pandey, Can Son Khoo, William Lotter, Yuanfang Guan, Thomas Yu, Andrew D. Trister, Stephen Morrell, Gustavo Stolovitzky, A. Gregory Sorensen, Clinton Fookes, Mehmet Eren Ahsen, David D. Cox, Jae Ho Sohn, Hao Du, Thomas Schaffter, Joseph H. Rothstein, Eduardo Castro, Joseph Y. Lo, Daniel L. Rubin, Obioma Pelka
Publikováno v:
JAMA Network Open
Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective: To evalua
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c12077daf27f336825279d2831286b1
https://doi.org/10.1001/jamanetworkopen.2020.0265
https://doi.org/10.1001/jamanetworkopen.2020.0265
Autor:
Miguel Hernandez Pampaloni, Dmytro Lituiev, Spencer C. Behr, Benjamin L. Franc, Lorenzo Nardo, Jae Ho Sohn, Yiming Ding, Roy Harnish, Robert R. Flavell, Shih-ying Huang, Hari Trivedi, Youngho Seo, Nathaniel W. Jenkins, Dexter Hadley, Carina Mari Aparici, Randall A. Hawkins, Michael Kawczynski, Kelly A. Zalocusky, Timothy P. Copeland, Mariam Aboian
Publikováno v:
Radiology, vol 290, iss 2
PURPOSE: To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 ((18)F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a907b1126e17566d8928eebdd2d0161
https://pubmed.ncbi.nlm.nih.gov/30398438
https://pubmed.ncbi.nlm.nih.gov/30398438