452 citations · 954 across the 7 of their papers we have counts for
7 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…
Validating transformers for redaction of text from electronic health records in real-world healthcare
Zeljko Kraljevic, Anthony Shek, Joshua Au Yeung +8
Protecting patient privacy in healthcare records is a top priority, and redaction is a commonly used method for obscuring directly identifiable information in text. Rule-based meth…
Current State of Community-Driven Radiological AI Deployment in Medical Imaging
Vikash Gupta, Barbaros Selnur Erdal, Carolina Ramirez +24
Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload o…
MONAI: An open-source framework for deep learning in healthcare
M. Jorge Cardoso, Wenqi Li, Richard Brown +53
Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagn…
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…
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…
Machine learning and glioma imaging biomarkers
Thomas Booth, Matthew Williams, Aysha Luis +3
Aim: To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring. Materials an…