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