Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Blumberg, Stefano B"'
Autor:
Lin, Hongxiang, Figini, Matteo, D'Arco, Felice, Ogbole, Godwin, Tanno, Ryutaro, Blumberg, Stefano B., Ronan, Lisa, Brown, Biobele J., Carmichael, David W., Lagunju, Ikeoluwa, Cross, Judith Helen, Fernandez-Reyes, Delmiro, Alexander, Daniel C.
Low-field (<1T) magnetic resonance imaging (MRI) scanners remain in widespread use in low- and middle-income countries (LMICs) and are commonly used for some applications in higher income countries e.g. for small child patients with obesity, claustro
Externí odkaz:
http://arxiv.org/abs/2304.13385
Autor:
Blumberg, Stefano B., Raví, Daniele, Xu, Mou-Cheng, Figini, Matteo, Kokkinos, Iasonas, Alexander, Daniel C.
Transposed convolution is crucial for generating high-resolution outputs, yet has received little attention compared to convolution layers. In this work we revisit transposed convolution and introduce a novel layer that allows us to place information
Externí odkaz:
http://arxiv.org/abs/2210.09446
This paper presents a data-driven, task-specific paradigm for experimental design, to shorten acquisition time, reduce costs, and accelerate the deployment of imaging devices. Current approaches in experimental design focus on model-parameter estimat
Externí odkaz:
http://arxiv.org/abs/2210.06891
Autor:
Lim, Jason P., Blumberg, Stefano B., Narayan, Neil, Epstein, Sean C., Alexander, Daniel C., Palombo, Marco, Slator, Paddy J.
Machine learning is a powerful approach for fitting microstructural models to diffusion MRI data. Early machine learning microstructure imaging implementations trained regressors to estimate model parameters in a supervised way, using synthetic train
Externí odkaz:
http://arxiv.org/abs/2210.02349
Autor:
Slumbers, Oliver, Mguni, David Henry, McAleer, Stephen Marcus, Blumberg, Stefano B., Wang, Jun, Yang, Yaodong
In order for agents in multi-agent systems (MAS) to be safe, they need to take into account the risks posed by the actions of other agents. However, the dominant paradigm in game theory (GT) assumes that agents are not affected by risk from other age
Externí odkaz:
http://arxiv.org/abs/2205.15434
Autor:
Xu, Mou-Cheng, Zhou, Yu-Kun, Jin, Chen, Blumberg, Stefano B, Wilson, Frederick J, deGroot, Marius, Alexander, Daniel C., Oxtoby, Neil P., Jacob, Joseph
We propose MisMatch, a novel consistency-driven semi-supervised segmentation framework which produces predictions that are invariant to learnt feature perturbations. MisMatch consists of an encoder and a two-head decoders. One decoder learns positive
Externí odkaz:
http://arxiv.org/abs/2203.10196
Autor:
Blumberg, Stefano B., Lin, Hongxiang, Grussu, Francesco, Zhou, Yukun, Figini, Matteo, Alexander, Daniel C.
We present PROSUB: PROgressive SUBsampling, a deep learning based, automated methodology that subsamples an oversampled data set (e.g. multi-channeled 3D images) with minimal loss of information. We build upon a recent dual-network approach that won
Externí odkaz:
http://arxiv.org/abs/2203.09268
Autor:
Zhou, Yukun, Xu, Moucheng, Hu, Yipeng, Blumberg, Stefano B., Zhao, An, Wagner, Siegfried K., Keane, Pearse A., Alexander, Daniel C.
Estimating clinically-relevant vascular features following vessel segmentation is a standard pipeline for retinal vessel analysis, which provides potential ocular biomarkers for both ophthalmic disease and systemic disease. In this work, we integrate
Externí odkaz:
http://arxiv.org/abs/2203.06425
Autor:
Zhou, Yukun, Xu, MouCheng, Hu, Yipeng, Blumberg, Stefano B., Zhao, An, Wagner, Siegfried K., Keane, Pearse A., Alexander, Daniel C.
Publikováno v:
In Medical Image Analysis April 2024 93
Autor:
Fu, Yunguan, Brown, Nina Montaña, Saeed, Shaheer U., Casamitjana, Adrià, Baum, Zachary M. C., Delaunay, Rémi, Yang, Qianye, Grimwood, Alexander, Min, Zhe, Blumberg, Stefano B., Iglesias, Juan Eugenio, Barratt, Dean C., Bonmati, Ester, Alexander, Daniel C., Clarkson, Matthew J., Vercauteren, Tom, Hu, Yipeng
DeepReg (https://github.com/DeepRegNet/DeepReg) is a community-supported open-source toolkit for research and education in medical image registration using deep learning.
Comment: Accepted in The Journal of Open Source Software (JOSS)
Comment: Accepted in The Journal of Open Source Software (JOSS)
Externí odkaz:
http://arxiv.org/abs/2011.02580