Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Samuel Remedios"'
Autor:
Corey W. Bown, Omair A. Khan, Dandan Liu, Kimberly R. Pechman, Samuel Remedios, L. Taylor Davis, Michelle Houston, Katherine A. Gifford, Timothy J. Hohman, Bennett A. Landman, Angela L. Jefferson
Publikováno v:
Alzheimer's & Dementia. 18
Autor:
Larry T Davis, Sneha Lingam, Lucas W. Remedios, Riqiang Gao, Bennett A. Landman, Samuel Remedios, Stephen W. Clark
Publikováno v:
Medical Physics. 48:6060-6068
Purpose Artificial intelligence diagnosis and triage of large vessel occlusion may quicken clinical response for a subset of time-sensitive acute ischemic stroke patients, improving outcomes. Differences in architectural elements within data-driven c
Autor:
Corey W. Bown, Omair A. Khan, Dandan Liu, Samuel Remedios, Kimberly R. Pechman, Matthew Schrag, L. Taylor Davis, James G. Terry, Sangeeta Nair, J. Jeffrey Carr, Katherine A. Gifford, Bennett A. Landman, Timothy J. Hohman, Angela L. Jefferson
Publikováno v:
Alzheimer's & Dementia. 17
Autor:
Virginia Fernandez, Jelmer M. Wolterink, David Wiesner, Samuel Remedios, Lianrui Zuo, Adrià Casamitjana
This book constitutes the refereed proceedings of the 9th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2024, held in conjunction with the 27th International conference on Medical Image Computing and Computer Assiste
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030781903
IPMI
IPMI
To super-resolve the through-plane direction of a multi-slice 2D magnetic resonance (MR) image, its slice selection profile can be used as the degeneration model from high resolution (HR) to low resolution (LR) to create paired data when training a s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7b0d102f17be8bd2bd84f035c4ab1386
https://doi.org/10.1007/978-3-030-78191-0_9
https://doi.org/10.1007/978-3-030-78191-0_9
Autor:
Carlo Pierpaoli, François Rheault, Kurt G. Schilling, Maxime Descoteaux, Laurent Petit, Bennett A. Landman, Samuel Remedios, Adam W. Anderson
Publikováno v:
Brain Structure and Function
Brain Structure and Function, Springer Verlag, 2020, 225 (8), pp.2387-2402. ⟨10.1007/s00429-020-02129-z⟩
Brain Structure and Function, 2020, 225 (8), pp.2387-2402. ⟨10.1007/s00429-020-02129-z⟩
Brain Struct Funct
Brain Structure and Function, Springer Verlag, 2020, 225 (8), pp.2387-2402. ⟨10.1007/s00429-020-02129-z⟩
Brain Structure and Function, 2020, 225 (8), pp.2387-2402. ⟨10.1007/s00429-020-02129-z⟩
Brain Struct Funct
International audience; MR Tractography, which is based on MRI measures of water diffusivity, is currently the only method available for noninvasive reconstruction of fiber pathways in the brain. However, it has several fundamental limitations that c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9018bb2f692493b0e330e2f7a3854d12
https://hal.archives-ouvertes.fr/hal-03004295
https://hal.archives-ouvertes.fr/hal-03004295
Autor:
Justin A. Blaber, Yuankai Huo, Yurui Gao, Samuel Remedios, Bennett A. Landman, Vishwesh Nath, Kurt G. Schilling, Roza G. Bayrak, Adam W. Anderson
Publikováno v:
ISBI
Histological analysis is typically the gold standard for validating measures of tissue microstructure derived from magnetic resonance imaging (MRI) contrasts. However, most histological investigations are inherently 2-dimensional (2D), due to increas
Autor:
Zihao Wu, Mayur B. Patel, Snehashis Roy, Camilo Bermudez, Samuel Remedios, John A. Butman, Cailey I. Kerley, Dzung L. Pham, Bennett A. Landman
Publikováno v:
Medical Imaging: Image Processing
Proc SPIE Int Soc Opt Eng
Proc SPIE Int Soc Opt Eng
Multiple instance learning (MIL) is a supervised learning methodology that aims to allow models to learn instance class labels from bag class labels, where a bag is defined to contain multiple instances. MIL is gaining traction for learning from weak
Autor:
Samuel Remedios, Maureen McHugo, Catherine Lebel, Stephan Heckers, Bennett A. Landman, Justin A. Blaber, Yuankai Huo, Camilo Bermudez, Jess E. Reynolds
Publikováno v:
Medical Imaging: Image Processing
Generalizability is an important problem in deep neural networks, especially in the context of the variability of data acquisition in clinical magnetic resonance imaging (MRI). Recently, the Spatially Localized Atlas Network Tiles (SLANT) approach ha
Publikováno v:
Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning ISBN: 9783030605476
DART/DCL@MICCAI
Lect Notes Monogr Ser
DART/DCL@MICCAI
Lect Notes Monogr Ser
Multi-site training methods for artificial neural networks are of particular interest to the medical machine learning community primarily due to the difficulty of data sharing between institutions. However, contemporary multi-site techniques such as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4bf789972308624bb4251d3939498277
https://doi.org/10.1007/978-3-030-60548-3_17
https://doi.org/10.1007/978-3-030-60548-3_17