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pro vyhledávání: '"Epstein, Sean C."'
Autor:
Parker, Christopher S., Schroder, Anna, Epstein, Sean C., Cole, James, Alexander, Daniel C., Zhang, Hui
Purpose: Previous quantitative MR imaging studies using self-supervised deep learning have reported biased parameter estimates at low SNR. Such systematic errors arise from the choice of Mean Squared Error (MSE) loss function for network training, wh
Externí odkaz:
http://arxiv.org/abs/2307.07072
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
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2024)
Deep learning (DL) is gaining popularity as a parameter estimation method for quantitative MRI. A range of competing implementations have been proposed, relying on either supervised or self-supervised learning. Self-supervised approaches, sometimes r
Externí odkaz:
http://arxiv.org/abs/2205.05587
This paper proposes a task-driven computational framework for assessing diffusion MRI experimental designs which, rather than relying on parameter-estimation metrics, directly measures quantitative task performance. Traditional computational experime
Externí odkaz:
http://arxiv.org/abs/2103.08438