1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
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…
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…