78 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University College LondonGB16 papers
- King's College LondonGB6 papers
- Wellcome / EPSRC Centre for Interventional and Surgical SciencesGB4 papers
- Cambridge University Hospitals NHS Foundation TrustGB2 papers
- National Hospital for Neurology and NeurosurgeryGB2 papers
- UCL Biomedical Research CentreGB2 papers
- University of OxfordGB2 papers
- Carnegie Mellon UniversityUS1 paper
- Centre for Clinical InterventionsAU1 paper
- Ege UniversityTR1 paper
- Guy's and St Thomas' NHS Foundation TrustGB1 paper
- Keele UniversityGB1 paper
16 papers
Evaluation of Uncertainty-Aware Multi-Software Ensembles for Hippocampal Segmentation
Gabriel Oliveira-Stahl, Anna Schroder, James Moggridge +6
Accurate hippocampal segmentation can be a useful tool for diagnosing and monitoring neurological conditions such as Alzheimer's disease and epilepsy. While numerous automated segm…
Real-time 3D Ultrasonic Needle Tracking with a Photoacoustic Beacon
Christian Baker, Weidong Liang, Richard Colchester +7
Many minimally invasive procedures, such as core needle biopsy of focal liver lesions, nerve blocks, and fetal and vascular interventions, are typically performed under ultrasound…
KEVS: Enhancing Segmentation of Visceral Adipose Tissue in Pre-Cystectomy CT with Gaussian Kernel Density Estimation
Thomas Boucher, Nicholas Tetlow, Annie Fung +5
Purpose: The distribution of visceral adipose tissue (VAT) in cystectomy patients is indicative of the incidence of post-operative complications. Existing VAT segmentation methods…
Machine learning algorithms to predict the risk of rupture of intracranial aneurysms: a systematic review
Karan Daga, Siddharth Agarwal, Zaeem Moti +5
Purpose: Subarachnoid haemorrhage is a potentially fatal consequence of intracranial aneurysm rupture, however, it is difficult to predict if aneurysms will rupture. Prophylactic t…
Jump stochastic differential equations for the characterisation of the Bragg peak in proton beam radiotherapy
Alastair Crossley, Karen Habermann, Emma Horton +3
Proton beam radiotherapy stands at the forefront of precision cancer treatment, leveraging the unique physical interactions of proton beams with human tissue to deliver minimal dos…
OMG-Net: A Deep Learning Framework Deploying Segment Anything to Detect Pan-Cancer Mitotic Figures from Haematoxylin and Eosin-Stained Slides
Zhuoyan Shen, Mikael Simard, Douglas Brand +14
Mitotic activity is an important feature for grading several cancer types. Counting mitotic figures (MFs) is a time-consuming, laborious task prone to inter-observer variation. Ina…
Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology
Nur Yildirim, Hannah Richardson, Maria T. Wetscherek +18
Recent advances in AI combine large language models (LLMs) with vision encoders that bring forward unprecedented technical capabilities to leverage for a wide range of healthcare a…
Interpolation-Split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance
Wing Keung Cheung, Ashkan Pakzad, Nesrin Mogulkoc +14
The morphology and distribution of airway tree abnormalities enables diagnosis and disease characterisation across a variety of chronic respiratory conditions. In this regard, airw…
Going faster to see further: GPU-accelerated value iteration and simulation for perishable inventory control using JAX
Joseph Farrington, Kezhi Li, Wai Keong Wong +1
Value iteration can find the optimal replenishment policy for a perishable inventory problem, but is computationally demanding due to the large state spaces that are required to re…
Image quality assessment for machine learning tasks using meta-reinforcement learning
Shaheer U. Saeed, Yunguan Fu, Vasilis Stavrinides +8
In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability. When the task is per…
Morphological Change Forecasting for Prostate Glands using Feature-based Registration and Kernel Density Extrapolation
Qianye Yang, Tom Vercauteren, Yunguan Fu +7
Organ morphology is a key indicator for prostate disease diagnosis and prognosis. For instance, In longitudinal study of prostate cancer patients under active surveillance, the vol…
Longitudinal Image Registration with Temporal-order and Subject-specificity Discrimination
Qianye Yang, Yunguan Fu, Francesco Giganti +6
Morphological analysis of longitudinal MR images plays a key role in monitoring disease progression for prostate cancer patients, who are placed under an active surveillance progra…
Prostate motion modelling using biomechanically-trained deep neural networks on unstructured nodes
Shaheer U. Saeed, Zeike A. Taylor, Mark A. Pinnock +3
In this paper, we propose to train deep neural networks with biomechanical simulations, to predict the prostate motion encountered during ultrasound-guided interventions. In this a…
Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions
Zach Eaton-Rosen, Felix Bragman, Sotirios Bisdas +2
Automated medical image segmentation, specifically using deep learning, has shown outstanding performance in semantic segmentation tasks. However, these methods rarely quantify the…
Scalable multimodal convolutional networks for brain tumour segmentation
Lucas Fidon, Wenqi Li, Luis C. Garcia-Peraza-Herrera +4
Brain tumour segmentation plays a key role in computer-assisted surgery. Deep neural networks have increased the accuracy of automatic segmentation significantly, however these mod…
Performance of three-photon PET imaging: Monte Carlo simulations
Krzysztof Kacperski, Nicholas M. Spyrou
We have recently introduced the idea of making use of three-photon positron annihilations in positron emission tomography. In this paper the basic characteristics of the three-gamm…