390 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- King's College LondonGB14 papers
- University College LondonGB5 papers
- King's College SchoolGB4 papers
- St Thomas' HospitalGB4 papers
- Guy's and St Thomas' NHS Foundation TrustGB2 papers
- Imperial College Healthcare NHS TrustGB2 papers
- Alberta Machine Intelligence InstituteCA1 paper
- American College of RadiologyUS1 paper
- Aminu Kano Teaching HospitalNG1 paper
- Aristotle University of ThessalonikiGR1 paper
- Asan Medical CenterKR1 paper
- Athinoula A. Martinos Center for Biomedical ImagingUS1 paper
16 papers
From artificial to organic: Rethinking the roots of intelligence for digital health
Prajwal Ghimire, Keyoumars Ashkan
The term artificial implies an inherent dichotomy from the natural or organic. However, AI, as we know it, is a product of organic ingenuity: designed, implemented, and iteratively…
Quantification of dual-state 5-ALA-induced PpIX fluorescence: Methodology and validation in tissue-mimicking phantoms
Silvère Ségaud, Charlie Budd, Matthew Elliot +4
Quantification of protoporphyrin IX (PpIX) fluorescence in human brain tumours has the potential to significantly improve patient outcomes in neuro-oncology, but represents a formi…
X-RAFT: Cross-Modal Non-Rigid Registration of Blue and White Light Neurosurgical Hyperspectral Images
Charlie Budd, Silvère Ségaud, Matthew Elliot +4
Integration of hyperspectral imaging into fluorescence-guided neurosurgery has the potential to improve surgical decision making by providing quantitative fluorescence measurements…
A generalisable head MRI defacing pipeline: Evaluation on 2,566 meningioma scans
Lorena Garcia-Foncillas Macias, Aaron Kujawa, Aya Elshalakany +2
Reliable MRI defacing techniques to safeguard patient privacy while preserving brain anatomy are critical for research collaboration. Existing methods often struggle with incomplet…
Radiogenomic biomarkers for immunotherapy in glioblastoma: A systematic review of magnetic resonance imaging studies
Prajwal Ghimire, Ben Kinnersley, Golestan Karami +5
Immunotherapy is an effective precision medicine treatment for several cancers. Imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients may serve as pr…
Proton Resonance Shift Thermometry: A review of modern clinical practices
J. Blackwell, M. J. Krasny, A. O'Brien +4
Magnetic Resonance Imaging (MRI) has become a popular modality in guiding minimally invasive thermal therapies, due to its advanced, non-ionizing, imaging capabilities and its abil…
Spatial gradient consistency for unsupervised learning of hyperspectral demosaicking: Application to surgical imaging
Peichao Li, Muhammad Asad, Conor Horgan +3
Hyperspectral imaging has the potential to improve intraoperative decision making if tissue characterisation is performed in real-time and with high-resolution. Hyperspectral snaps…
Discharge Summary Hospital Course Summarisation of In Patient Electronic Health Record Text with Clinical Concept Guided Deep Pre-Trained Transformer Models
Thomas Searle, Zina Ibrahim, James Teo +1
Brief Hospital Course (BHC) summaries are succinct summaries of an entire hospital encounter, embedded within discharge summaries, written by senior clinicians responsible for the…
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…
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction
Peichao Li, Michael Ebner, Philip Noonan +5
Hyperspectral imaging is one of the most promising techniques for intraoperative tissue characterisation. Snapshot mosaic cameras, which can capture hyperspectral data in a single…
Estimating Redundancy in Clinical Text
Thomas Searle, Zina Ibrahim, James Teo +1
The current mode of use of Electronic Health Record (EHR) elicits text redundancy. Clinicians often populate new documents by duplicating existing notes, then updating accordingly.…
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…
Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning
Alexandros Papadopoulos, Konstantinos Kyritsis, Lisa Klingelhoefer +3
Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old, causing symptoms that are subtle at first, but whose…
An Update on Machine Learning in Neuro-oncology Diagnostics
Thomas Booth
Imaging biomarkers in neuro-oncology are used for diagnosis, prognosis and treatment response monitoring. Magnetic resonance imaging is typically used throughout the patient pathwa…
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…