1 citations · 3 across the 12 of their papers we have counts for
12 papers
Confidence is Not Reliability: Rethinking MC Dropout in Brain Tumour Segmentation
Xin Ci Wong, Duygu Sarikaya, Kieran Zucker +2
Glioma segmentation in multiparametric MRI is a critical component of treatment planning. A segmentation model that fails silently on treatment-critical sub-regions represents a pa…
Can Diffusion Models Bridge the Domain Gap in Cardiac MR Imaging?
Xin Ci Wong, Duygu Sarikaya, Kieran Zucker +2
Magnetic resonance (MR) imaging, including cardiac MR, is prone to domain shift due to variations in imaging devices and acquisition protocols. This challenge limits the deployment…
Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtyping
Jack Breen, Katie Allen, Kieran Zucker +2
Computer vision models are increasingly capable of classifying ovarian epithelial cancer subtypes, but they differ from pathologists by processing small tissue patches at a single…
A Comprehensive Evaluation of Histopathology Foundation Models for Ovarian Cancer Subtype Classification
Jack Breen, Katie Allen, Kieran Zucker +3
Large pretrained transformers are increasingly being developed as generalised foundation models which can underpin powerful task-specific artificial intelligence models. Histopatho…
Reducing Histopathology Slide Magnification Improves the Accuracy and Speed of Ovarian Cancer Subtyping
Jack Breen, Katie Allen, Kieran Zucker +2
Artificial intelligence has found increasing use for ovarian cancer morphological subtyping from histopathology slides, but the optimal magnification for computational interpretati…
Predicting Ovarian Cancer Treatment Response in Histopathology using Hierarchical Vision Transformers and Multiple Instance Learning
Jack Breen, Katie Allen, Kieran Zucker +3
For many patients, current ovarian cancer treatments offer limited clinical benefit. For some therapies, it is not possible to predict patients' responses, potentially exposing the…
Generative Adversarial Networks for Stain Normalisation in Histopathology
Jack Breen, Kieran Zucker, Katie Allen +2
The rapid growth of digital pathology in recent years has provided an ideal opportunity for the development of artificial intelligence-based tools to improve the accuracy and effic…
Artificial Intelligence in Ovarian Cancer Histopathology: A Systematic Review
Jack Breen, Katie Allen, Kieran Zucker +5
Purpose - To characterise and assess the quality of published research evaluating artificial intelligence (AI) methods for ovarian cancer diagnosis or prognosis using histopatholog…
Efficient subtyping of ovarian cancer histopathology whole slide images using active sampling in multiple instance learning
Jack Breen, Katie Allen, Kieran Zucker +3
Weakly-supervised classification of histopathology slides is a computationally intensive task, with a typical whole slide image (WSI) containing billions of pixels to process. We p…
Learning disentangled representations for explainable chest X-ray classification using Dirichlet VAEs
Rachael Harkness, Alejandro F Frangi, Kieran Zucker +1
This study explores the use of the Dirichlet Variational Autoencoder (DirVAE) for learning disentangled latent representations of chest X-ray (CXR) images. Our working hypothesis i…
The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays
Rachael Harkness, Geoff Hall, Alejandro F Frangi +2
Since the emergence of COVID-19, deep learning models have been developed to identify COVID-19 from chest X-rays. With little to no direct access to hospital data, the AI community…
Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images
Jack Breen, Kieran Zucker, Nicolas M. Orsi +1
Breast cancer is the most commonly diagnosed cancer worldwide, with over two million new cases each year. During diagnostic tumour grading, pathologists manually count the number o…