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Inline AI: Open-source Deep Learning Inference for Cardiac MR

Autor: Xue, Hui, Davies, Rhodri H, Howard, James, Shiwani, Hunain, Rehman, Azaan, Pierce, Iain, Procter, Henry, Fontana, Marianna, Moon, James C, Levelt, Eylem, Kellman, Peter

Cardiac Magnetic Resonance (CMR) is established as a non-invasive imaging technique for evaluation of heart function, anatomy, and myocardial tissue characterization. Quantitative biomarkers are central for diagnosis and management of heart disease.

Externí odkaz: http://arxiv.org/abs/2404.02384

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Imaging transformer for MRI denoising with the SNR unit training: enabling generalization across field-strengths, imaging contrasts, and anatomy

Autor: Xue, Hui, Hooper, Sarah, Rehman, Azaan, Pierce, Iain, Treibel, Thomas, Davies, Rhodri, Bandettini, W Patricia, Ramasawmy, Rajiv, Javed, Ahsan, Zhu, Zheren, Yang, Yang, Moon, James, Campbell, Adrienne, Kellman, Peter

The ability to recover MRI signal from noise is key to achieve fast acquisition, accurate quantification, and high image quality. Past work has shown convolutional neural networks can be used with abundant and paired low and high-SNR images for train

Externí odkaz: http://arxiv.org/abs/2404.02382

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Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation

Autor: Zhao, Yidong, Tourais, Joao, Pierce, Iain, Nitsche, Christian, Treibel, Thomas A., Weingärtner, Sebastian, Schweidtmann, Artur M., Tao, Qian

Publikováno v: Machine.Learning.for.Biomedical.Imaging. 2 (2024)

Deep learning (DL)-based methods have achieved state-of-the-art performance for many medical image segmentation tasks. Nevertheless, recent studies show that deep neural networks (DNNs) can be miscalibrated and overconfident, leading to "silent failu

Externí odkaz: http://arxiv.org/abs/2403.02311

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