390 citations · 596 across the 11 of their papers we have counts for
11 papers
Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium
Haris Shuaib, Gareth J Barker, Peter Sasieni +16
Objective: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep-learning models in gli…
Artificial Intelligence in the Autonomous Navigation of Endovascular Interventions: A Systematic Review
Harry Robertshaw, Lennart Karstensen, Benjamin Jackson +5
Purpose: Autonomous navigation of devices in endovascular interventions can decrease operation times, improve decision-making during surgery, and reduce operator radiation exposure…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
Automated triaging of head MRI examinations using convolutional neural networks
David A. Wood, Sina Kafiabadi, Ayisha Al Busaidi +9
The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI…
Machine Learning and Glioblastoma: Treatment Response Monitoring Biomarkers in 2021
Thomas Booth, Bernice Akpinar, Andrei Roman +10
The aim of the systematic review was to assess recently published studies on diagnostic test accuracy of glioblastoma treatment response monitoring biomarkers in adults, developed…
Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets
Reuben Dorent, Thomas Booth, Wenqi Li +5
Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been de…