Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Devi D, Nelakurti"'
Autor:
Xuan V. Nguyen, Devi D. Nelakurti, Engin Dikici, Sema Candemir, Daniel J. Boulter, Luciano M. Prevedello
Publikováno v:
Information, Vol 13, Iss 9, p 412 (2022)
Background: Distinguishing between the spinal cord and cerebrospinal fluid (CSF) non-invasively on CT is challenging due to their similar mass densities. We hypothesize that patch-based machine learning applied to dual-energy CT can accurately distin
Externí odkaz:
https://doaj.org/article/0037e61eee1747138f878ed3cf39b76b
Autor:
W Miguel, Disbennett, Tila M, Hawk, P Daniel, Rollins, Devi D, Nelakurti, Bailey E, Lucas, Matthew T, McPherson, Hannah M, Hylton, Ruben C, Petreaca
Publikováno v:
microPublication biology. 2022
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::26310c47fd624edf6bed532aa94f0e25
https://pubmed.ncbi.nlm.nih.gov/35622511
https://pubmed.ncbi.nlm.nih.gov/35622511
Publikováno v:
Cancers
Cancers, Vol 13, Iss 6274, p 6274 (2021)
Cancers; Volume 13; Issue 24; Pages: 6274
Cancers, Vol 13, Iss 6274, p 6274 (2021)
Cancers; Volume 13; Issue 24; Pages: 6274
Simple Summary Thousands of cancer genomes are now publicly available which has led to new insights into the underlying features of cancers. These include the identification of mutational signatures at both nucleotide and amino acid levels. Here, we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c1a3b27b5112ca9d7065869aeedadd9
https://pubmed.ncbi.nlm.nih.gov/34944895
https://pubmed.ncbi.nlm.nih.gov/34944895
Publikováno v:
Cancers
Volume 12
Issue 9
Cancers, Vol 12, Iss 2616, p 2616 (2020)
Volume 12
Issue 9
Cancers, Vol 12, Iss 2616, p 2616 (2020)
Simple Summary Cancers are characterized by accumulation of genetic mutations in key cell cycle regulators that alter or disable the function of these genes. Such mutations can be inherited or arise spontaneously during the life of the individual. Th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b846d6382b7e016f4ca5b29948acb50
Autor:
Mahmud Mossa-Basha, Xuan V. Nguyen, Murat Alp Oztek, Christina L. Brunnquell, Devi D Nelakurti, David R. Haynor, Luciano M. Prevedello
Publikováno v:
Topics in Magnetic Resonance Imaging.
Artificial intelligence, particularly deep learning, offers several possibilities to improve the quality or speed of image acquisition in magnetic resonance imaging (MRI). In this article, we briefly review basic machine learning concepts and discuss
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6b642d791f13d6e97eef19c2566a889a
https://doi.org/10.1097/rmr.0000000000000249
https://doi.org/10.1097/rmr.0000000000000249