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20132025
most citedWhat Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use

261 citations

Showing eess.IVShow all

5 papers · 1 filter

eess.IV2025

High Resolution Isotropic 3D Cine imaging with Automated Segmentation using Concatenated 2D Real-time Imaging and Deep Learning

Mark Wrobel, Michele Pascale, Tina Yao +5

Background: Conventional cardiovascular magnetic resonance (CMR) in paediatric and congenital heart disease uses 2D, breath-hold, balanced steady state free precession (bSSFP) cine…

eess.IV2023★ 9 cited

Latent Disentanglement in Mesh Variational Autoencoders Improves the Diagnosis of Craniofacial Syndromes and Aids Surgical Planning

Simone Foti, Alexander J. Rickart, Bongjin Koo +9

The use of deep learning to undertake shape analysis of the complexities of the human head holds great promise. However, there have traditionally been a number of barriers to accur…

eess.IV2023★ 42 cited

Low-field magnetic resonance image enhancement via stochastic image quality transfer

Hongxiang Lin, Matteo Figini, Felice D'Arco +10

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…

eess.IV2020★ 4 cited

Image Quality Transfer Enhances Contrast and Resolution of Low-Field Brain MRI in African Paediatric Epilepsy Patients

Matteo Figini, Hongxiang Lin, Godwin Ogbole +10

1.5T or 3T scanners are the current standard for clinical MRI, but low-field (<1T) scanners are still common in many lower- and middle-income countries for reasons of cost and robu…

eess.IV2019★ 2 cited

Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator

Hongxiang Lin, Matteo Figini, Ryutaro Tanno +10

MR images scanned at low magnetic field (T) have lower resolution in the slice direction and lower contrast, due to a relatively small signal-to-noise ratio (SNR) than those fr…