176 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Imperial College LondonGB100 papers
- King's College LondonGB6 papers
- University of OxfordGB6 papers
- University College LondonGB5 papers
- University of CambridgeGB5 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- Technical University of MunichDE4 papers
- Institute for High Energy PhysicsES3 papers
- Institute of Group AnalysisGB3 papers
- Lancaster UniversityGB3 papers
- MRC London Institute of Medical SciencesGB3 papers
- Nanjing University of Science and TechnologyCN3 papers
5 papers · 1 filter
In-Vivo Hyperspectral Human Brain Image Database for Brain Cancer Detection
H. Fabelo, S. Ortega, A. Szolna +25
The use of hyperspectral imaging for medical applications is becoming more common in recent years. One of the main obstacles that researchers find when developing hyperspectral alg…
Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
Yang Nan, Xiaodan Xing, Shiyi Wang +38
Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway trees remains…
Identifying visible tissue in intraoperative ultrasound: a method and application
Alistair Weld, Luke Dixon, Giulio Anichini +4
Purpose: Intraoperative ultrasound scanning is a demanding visuotactile task. It requires operators to simultaneously localise the ultrasound perspective and manually perform sligh…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
VS-Net: Variable splitting network for accelerated parallel MRI reconstruction
Jinming Duan, Jo Schlemper, Chen Qin +7
In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high…