Feb. 28, 2024, 5:47 a.m. | Andrew Mao, Sebastian Flassbeck, Jakob Assl\"ander

cs.CV updates on arXiv.org arxiv.org

arXiv:2312.11468v2 Announce Type: replace-cross
Abstract: Purpose: To develop neural network (NN)-based quantitative MRI parameter estimators with minimal bias and a variance close to the Cram\'er-Rao bound.
Theory and Methods: We generalize the mean squared error loss to control the bias and variance of the NN's estimates, which involves averaging over multiple noise realizations of the same measurements during training. Bias and variance properties of the resulting NNs are studied for two neuroimaging applications.
Results: In simulations, the proposed strategy reduces …

abstract arxiv bias control cs.cv error loss mean mri multiple network networks neural network neural networks physics.med-ph quantitative theory type variance

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