1 citations · 3 across the 12 of their papers we have counts for
6 papers · 1 filter
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…