Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Ansó, Nil Stolt"'
Autor:
McGinnis, Julian, Shit, Suprosanna, Li, Hongwei Bran, Sideri-Lampretsa, Vasiliki, Graf, Robert, Dannecker, Maik, Pan, Jiazhen, Ansó, Nil Stolt, Mühlau, Mark, Kirschke, Jan S., Rueckert, Daniel, Wiestler, Benedikt
Clinical routine and retrospective cohorts commonly include multi-parametric Magnetic Resonance Imaging; however, they are mostly acquired in different anisotropic 2D views due to signal-to-noise-ratio and scan-time constraints. Thus acquired views s
Externí odkaz:
http://arxiv.org/abs/2303.15065
Autor:
Ansó, Nil Stolt
Large enough computed tomography (CT) data sets to train supervised deep models are often hard to come by. One contributing issue is the amount of manual labor that goes into creating ground truth labels, specially for volumetric data. In this resear
Externí odkaz:
http://arxiv.org/abs/2001.00666
Autor:
Ansó, Nil Stolt
The Sampled Policy Gradient (SPG) algorithm is a new offline actor-critic variant that samples in the action space to approximate the policy gradient. It does so by using the critic to evaluate the sampled actions. SPG offers theoretical promise over
Externí odkaz:
http://arxiv.org/abs/1910.03728
In this paper, a new offline actor-critic learning algorithm is introduced: Sampled Policy Gradient (SPG). SPG samples in the action space to calculate an approximated policy gradient by using the critic to evaluate the samples. This sampling allows
Externí odkaz:
http://arxiv.org/abs/1809.05763
Autor:
McGinnis, Julian, Shit, Suprosanna, Li, Hongwei Bran, Sideri-Lampretsa, Vasiliki, Graf, Robert, Dannecker, Maik, Pan, Jiazhen, Ansó, Nil Stolt, Mühlau, Mark, Kirschke, Jan S., Rueckert, Daniel, Wiestler, Benedikt
Clinical routine and retrospective cohorts commonly include multi-parametric Magnetic Resonance Imaging; however, they are mostly acquired in different anisotropic 2D views due to signal-to-noise-ratio and scan-time constraints. Thus acquired views s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::45099fd22271a88ec703d618b99c4fd3