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most citedA Comprehensive Evaluation of Histopathology Foundation Models for Ovarian Cancer Subtype Classification

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

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eess.IV2024★ 1 cited

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…

eess.IV2024★ 1 cited

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…

eess.IV2023

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…

eess.IV2023

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…

eess.IV2023

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…

eess.IV2023★ 1 cited

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…

eess.IV2023

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…

eess.IV2021

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…