Zobrazeno 1 - 10
of 94
pro vyhledávání: '"Hassan M. Fathallah-Shaykh"'
Autor:
Yassine Barhoumi, Abdul Hamid Fattah, Nidhal Bouaynaya, Fanny Moron, Jinsuh Kim, Hassan M. Fathallah-Shaykh, Rouba A. Chahine, Houman Sotoudeh
Publikováno v:
Diagnostics, Vol 14, Iss 11, p 1066 (2024)
Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the
Externí odkaz:
https://doaj.org/article/3904e0d4fa734bbf93b4ca42a3ce952e
Autor:
Ian E. Nielsen, Ravi P. Ramachandran, Nidhal Bouaynaya, Hassan M. Fathallah-Shaykh, Ghulam Rasool
Publikováno v:
IEEE Access, Vol 11, Pp 82556-82569 (2023)
The expansion of explainable artificial intelligence as a field of research has generated numerous methods of visualizing and understanding the black box of a machine learning model. Attribution maps are commonly used to highlight the parts of the in
Externí odkaz:
https://doaj.org/article/4b602330428242a7ae5a6c7c6207a24b
Autor:
Fabio Raman, Alexander Mullen, Matthew Byrd, Sejong Bae, Jinsuh Kim, Houman Sotoudeh, Fanny E. Morón, Hassan M. Fathallah-Shaykh
Publikováno v:
Cancers, Vol 15, Iss 13, p 3274 (2023)
Purpose: The Response Assessment in Neuro-Oncology (RANO) criteria for lower-grade gliomas (LGGs) define tumor progression as ≥25% change in the T2/FLAIR signal area based on an operator’s discretion of the perpendicular diameter of the largest t
Externí odkaz:
https://doaj.org/article/29e4a57ea2cd4f4691686076031d86d4
Publikováno v:
BioData Mining, Vol 12, Iss 1, Pp 1-14 (2019)
Abstract Background Most existing algorithms for modeling and analyzing molecular networks assume a static or time-invariant network topology. Such view, however, does not render the temporal evolution of the underlying biological process as molecula
Externí odkaz:
https://doaj.org/article/7647d9f110144b7eb314d8a00ab5bcfb
Publikováno v:
Frontiers in Computational Neuroscience, Vol 13 (2019)
Magnetic resonance images of brain tumors are routinely used in neuro-oncology clinics for diagnosis, treatment planning, and post-treatment tumor surveillance. Currently, physicians spend considerable time manually delineating different structures o
Externí odkaz:
https://doaj.org/article/6b04ad8241ab458ba6c69574848c32ff
Autor:
Martin R. Johnson, L. Burt Nabors, Hassan M. Fathallah-Shaykh, Alan Cantor, Cheryl A. Palmer, John Fiveash, Jessica M. Grunda
Supplementary Data from Rationally Designed Pharmacogenomic Treatment Using Concurrent Capecitabine and Radiotherapy for Glioblastoma; Gene Expression Profiles Associated with Outcome
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::03c11747eddac034c592e700e1814044
https://doi.org/10.1158/1078-0432.22442523
https://doi.org/10.1158/1078-0432.22442523
Autor:
Martin R. Johnson, L. Burt Nabors, Hassan M. Fathallah-Shaykh, Alan Cantor, Cheryl A. Palmer, John Fiveash, Jessica M. Grunda
Purpose: Previous preclinical studies suggested that concurrent capecitabine and radiation could be an effective new treatment modality for glioblastoma (GBM). In the current study, we investigate toxicity and response to this regimen and explore ass
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6855070a92325032c848437a2543ca1f
https://doi.org/10.1158/1078-0432.c.6519237.v1
https://doi.org/10.1158/1078-0432.c.6519237.v1
Publikováno v:
IEEE Transactions on Signal Processing. 69:4669-4684
Deep neural networks (DNNs) have surpassed human-level accuracy in various learning tasks. However, unlike humans who have a natural cognitive intuition for probabilities, DNNs cannot express their uncertainty in the output decisions. This limits the
Publikováno v:
BioData Mining, Vol 12, Iss 1, Pp 1-14 (2019)
BioData Mining
BioData Mining
Background Most existing algorithms for modeling and analyzing molecular networks assume a static or time-invariant network topology. Such view, however, does not render the temporal evolution of the underlying biological process as molecular network
Autor:
Wenyin Shi, Yoon Seong Choi, Adam E. Flanders, Sung Soo Ahn, Michel Bilello, Carmen Balana, Donald M. O'Rourke, Anahita Fathi Kazerooni, Gregory S. Alexander, Suyash Mohan, Joshua D. Palmer, Ujjwal Baid, Spyridon Bakas, Gaurav Shukla, Jong Hee Chang, Stephen J Bagley, Brent D Griffith, Hamed Akbari, Joseph Lombardo, Hassan M. Fathallah-Shaykh, Arash Nazeri, Jaume Capellades, Christos Davatzikos, Andrew E. Sloan, Mikhail Milchenko, Seung Koo Lee, Ayesha S Ali, William Taylor, Spencer Liem, Rivka R. Colen, Daniel S. Marcus, Pamela LaMontagne, Vachan Vadmal, Jose A Garcia, Abhishek Mahajan, MacLean Nasrallah, Kristin Waite, Rajan Jain, Jill S. Barnholtz-Sloan, Thomas C. Booth, Robert A. Lustig, Matthew Lee, Chaitra Bavde, Josep Puig, Santiago Cepeda, Steven Brem, Chiharu Sako, Laila M. Poisson, Murat Ak, Adam P. Dicker
Publikováno v:
Neuro-Oncology. 23:vi132-vi133
PURPOSE Multi-parametric MRI and artificial intelligence (AI) methods were previously used to predict peritumoral neoplastic cell infiltration and risk of future recurrence in glioblastoma, in single-institution studies. We hypothesize that important