Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Assaf Hoogi"'
Autor:
Guy Gaziv, Roman Beliy, Niv Granot, Assaf Hoogi, Francesca Strappini, Tal Golan, Michal Irani
Publikováno v:
NeuroImage, Vol 254, Iss , Pp 119121- (2022)
Reconstructing natural images and decoding their semantic category from fMRI brain recordings is challenging. Acquiring sufficient pairs of images and their corresponding fMRI responses, which span the huge space of natural images, is prohibitive. We
Externí odkaz:
https://doaj.org/article/de04c565f2864f8f9116d8fbdf1c052b
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031250651
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::018cc3ac89057c74b1e0168229c62683
https://doi.org/10.1007/978-3-031-25066-8_10
https://doi.org/10.1007/978-3-031-25066-8_10
Autor:
Tarub S. Mabud, Aaron C. Eifler, Assaf Hoogi, Lawrence V. Hofmann, A. Reposar, Daniel L. Rubin, D. Cohn, V. Arendt
Publikováno v:
Journal of Vascular and Interventional Radiology. 31:270-275
Purpose An automated segmentation technique (AST) for computed tomography (CT) venography was developed to quantify measures of disease severity before and after stent placement in patients with left-sided nonthrombotic iliac vein compression. Materi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b7fd41e71e6482bbd915b024f0c738ab
https://doi.org/10.1016/j.jvir.2019.04.034
https://doi.org/10.1016/j.jvir.2019.04.034
Autor:
Guy Gaziv, Niv Granot, Francesca Strappini, Tal Golan, Roman Beliy, Michal Irani, Assaf Hoogi
Publikováno v:
bioRxiv
Reconstructing natural images and decoding their semantic category from fMRI brain recordings is challenging. Acquiring sufficient pairs of images and their corresponding fMRI responses, which span the huge space of natural images, is prohibitive. We
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4c658bce20158ca070da14117a19006a
https://pubmed.ncbi.nlm.nih.gov/35342004
https://pubmed.ncbi.nlm.nih.gov/35342004
Publikováno v:
IEEE journal of biomedical and health informatics. 24(9)
Extending the size of labeled corpora of medical reports is a major step towards a successful training of machine learning algorithms. Simulating new text reports is a key solution for reports augmentation, which extends the cohort size. However, tex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e4e14deb818456691dd282f969bf6c81
https://pubmed.ncbi.nlm.nih.gov/32324577
https://pubmed.ncbi.nlm.nih.gov/32324577
Autor:
Jarrett Rosenberg, Huaijun Wang, Isabelle Durot, Daniel L. Rubin, Assaf Hoogi, Aya Kamaya, Jianhua Zhou, Hersh Sagreiya, Dimitre Hristov, Ahmed El Kaffas, Alireza Akhbardeh, Jürgen K. Willmann, Albert Tseng
Publikováno v:
Scientific Reports
Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
There is a need for noninvasive repeatable biomarkers to detect early cancer treatment response and spare non-responders unnecessary morbidities and costs. Here, we introduce three-dimensional (3D) dynamic contrast enhanced ultrasound (DCE-US) perfus
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f08a4dfea83f95b93eeab6fefdcc2bfc
https://doi.org/10.1038/s41598-020-63810-1
https://doi.org/10.1038/s41598-020-63810-1
Publikováno v:
Journal of Digital Imaging
Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their sel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::26794e9beca04e93f1a3ae4094d02210
https://doi.org/10.1007/s10278-017-9983-4
https://doi.org/10.1007/s10278-017-9983-4
Autor:
Daniel L. Rubin, Assaf Hoogi, Debleena Sengupta, Brian Wilcox, Ali Hatamizadeh, Wuyue Lu, Demetri Terzopoulos
Publikováno v:
Machine Learning in Medical Imaging ISBN: 9783030326913
MLMI@MICCAI
MLMI@MICCAI
Lesion segmentation is an important problem in computer-assisted diagnosis that remains challenging due to the prevalence of low contrast, irregular boundaries that are unamenable to shape priors. We introduce Deep Active Lesion Segmentation (DALS),
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dbebb83018330ba29f65bb5029225147
https://doi.org/10.1007/978-3-030-32692-0_12
https://doi.org/10.1007/978-3-030-32692-0_12
Autor:
Demetri Terzopoulos, Daniel L. Rubin, Ali Hatamizadeh, Assaf Hoogi, Wuyue Lu, Brian Wilcox, Debleena Sengupta
Lesion segmentation is an important problem in computer-assisted diagnosis that remains challenging due to the prevalence of low contrast, irregular boundaries that are unamenable to shape priors. We introduce Deep Active Lesion Segmentation (DALS),
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5ed40c17e34c633f886140345a98164