5 citations · 10 across the 10 of their papers we have counts for
11 papers
An Automated Framework for Large-Scale Graph-Based Cerebrovascular Analysis
Daniele Falcetta, Liane S. Canas, Lorenzo Suppa +5
We present CaravelMetrics, a computational framework for automated cerebrovascular analysis that models vessel morphology through skeletonization-derived graph representations. The…
LIHE: Linguistic Instance-Split Hyperbolic-Euclidean Framework for Generalized Weakly-Supervised Referring Expression Comprehension
Xianglong Shi, Silin Cheng, Sirui Zhao +4
Existing Weakly-Supervised Referring Expression Comprehension (WREC) methods, while effective, are fundamentally limited by a one-to-one mapping assumption, hindering their ability…
A methodology for clinically driven interactive segmentation evaluation
Parhom Esmaeili, Virginia Fernandez, Pedro Borges +3
Interactive segmentation is a promising strategy for building robust, generalisable algorithms for volumetric medical image segmentation. However, inconsistent and clinically unrea…
Generalizable automated ischaemic stroke lesion segmentation with vision transformers
Chris Foulon, Robert Gray, James K. Ruffle +13
Ischaemic stroke, a leading cause of death and disability, critically relies on neuroimaging for characterising the anatomical pattern of injury. Diffusion-weighted imaging (DWI) p…
Deep generative computed perfusion-deficit mapping of ischaemic stroke
Chayanin Tangwiriyasakul, Pedro Borges, Guilherme Pombo +8
Focal deficits in ischaemic stroke result from impaired perfusion downstream of a critical vascular occlusion. While parenchymal lesions are traditionally used to predict clinical…
ReCAP: Recursive Cross Attention Network for Pseudo-Label Generation in Robotic Surgical Skill Assessment
Julien Quarez, Marc Modat, Sebastien Ourselin +2
In surgical skill assessment, the Objective Structured Assessments of Technical Skills (OSATS) and Global Rating Scale (GRS) are well-established tools for evaluating surgeons duri…
A self-supervised text-vision framework for automated brain abnormality detection
David A. Wood, Emily Guilhem, Sina Kafiabadi +13
Artificial neural networks trained on large, expert-labelled datasets are considered state-of-the-art for a range of medical image recognition tasks. However, categorically labelle…
Framework to generate perfusion map from CT and CTA images in patients with acute ischemic stroke: A longitudinal and cross-sectional study
Chayanin Tangwiriyasakul, Pedro Borges, Stefano Moriconi +6
Stroke is a leading cause of disability and death. Effective treatment decisions require early and informative vascular imaging. 4D perfusion imaging is ideal but rarely available…
DD-VNB: A Depth-based Dual-Loop Framework for Real-time Visually Navigated Bronchoscopy
Qingyao Tian, Huai Liao, Xinyan Huang +5
Real-time 6 DOF localization of bronchoscopes is crucial for enhancing intervention quality. However, current vision-based technologies struggle to balance between generalization t…
RAISE -- Radiology AI Safety, an End-to-end lifecycle approach
M. Jorge Cardoso, Julia Moosbauer, Tessa S. Cook +11
The integration of AI into radiology introduces opportunities for improved clinical care provision and efficiency but it demands a meticulous approach to mitigate potential risks a…
Neuradicon: operational representation learning of neuroimaging reports
Henry Watkins, Robert Gray, Adam Julius +10
Radiological reports typically summarize the content and interpretation of imaging studies in unstructured form that precludes quantitative analysis. This limits the monitoring of…