Jack Breen
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View article: Multiple Instance Learning for the Detection of Lymph Node and Omental Metastases in Carcinoma of the Ovaries, Fallopian Tubes and Peritoneum
Multiple Instance Learning for the Detection of Lymph Node and Omental Metastases in Carcinoma of the Ovaries, Fallopian Tubes and Peritoneum Open
Background/Objectives: Surgical pathology of tubo-ovarian and peritoneal cancer carries a well-recognised diagnostic workload, partly due to the large amount of non-primary tumour-related tissue requiring assessment for the presence of met…
View article: A comprehensive evaluation of histopathology foundation models for ovarian cancer subtype classification
A comprehensive evaluation of histopathology foundation models for ovarian cancer subtype classification Open
Histopathology foundation models show great promise across many tasks, but analyses have been limited by arbitrary hyperparameters. We report the most rigorous single-task validation study to date, specifically in the context of ovarian ca…
View article: Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtyping
Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtyping Open
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 resolution. Multi-resolution graph models leverage the spat…
View article: A Comprehensive Evaluation of Histopathology Foundation Models for Ovarian Cancer Subtype Classification
A Comprehensive Evaluation of Histopathology Foundation Models for Ovarian Cancer Subtype Classification Open
Large pretrained transformers are increasingly being developed as generalised foundation models which can underpin powerful task-specific artificial intelligence models. Histopathology foundation models show great promise across many tasks…
View article: Generative Adversarial Networks for Stain Normalisation in Histopathology
Generative Adversarial Networks for Stain Normalisation in Histopathology Open
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 efficiency of clinical diagnoses. One of the significant roadblo…
View article: Reducing Histopathology Slide Magnification Improves the Accuracy and Speed of Ovarian Cancer Subtyping
Reducing Histopathology Slide Magnification Improves the Accuracy and Speed of Ovarian Cancer Subtyping Open
Artificial intelligence has found increasing use for ovarian cancer morphological subtyping from histopathology slides, but the optimal magnification for computational interpretation is unclear. Higher magnifications offer abundant cytolog…
View article: Predicting Ovarian Cancer Treatment Response in Histopathology using Hierarchical Vision Transformers and Multiple Instance Learning
Predicting Ovarian Cancer Treatment Response in Histopathology using Hierarchical Vision Transformers and Multiple Instance Learning Open
For many patients, current ovarian cancer treatments offer limited clinical benefit. For some therapies, it is not possible to predict patients' responses, potentially exposing them to the adverse effects of treatment without any therapeut…
View article: #900 Comparative evaluation of ovarian carcinoma subtyping in primary versus interval debulking surgery specimen whole slide images using artificial intelligence
#900 Comparative evaluation of ovarian carcinoma subtyping in primary versus interval debulking surgery specimen whole slide images using artificial intelligence Open
Introduction/Background Artificial intelligence (AI) approaches applied to digital pathology have shown promise in supporting morphological differentiation of ovarian carcinoma subtypes from resection specimen whole slide images (WSIs). Ho…
View article: Generative Adversarial Networks for Stain Normalisation in Histopathology
Generative Adversarial Networks for Stain Normalisation in Histopathology Open
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 efficiency of clinical diagnoses. One of the significant roadblo…
View article: Artificial Intelligence in Ovarian Cancer Histopathology: A Systematic Review
Artificial Intelligence in Ovarian Cancer Histopathology: A Systematic Review Open
Purpose - To characterise and assess the quality of published research evaluating artificial intelligence (AI) methods for ovarian cancer diagnosis or prognosis using histopathology data. Methods - A search of PubMed, Scopus, Web of Scienc…
View article: Efficient subtyping of ovarian cancer histopathology whole slide images using active sampling in multiple instance learning
Efficient subtyping of ovarian cancer histopathology whole slide images using active sampling in multiple instance learning Open
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 propose Discriminative Region Active Sampling for Multiple I…
View article: Mitosis domain generalization in histopathology images -- The MIDOG challenge
Mitosis domain generalization in histopathology images -- The MIDOG challenge Open
The density of mitotic figures within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of mitotic figures by pathologists is known to be subject to a stron…
View article: Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images
Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images Open
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 of dividing cells (mitotic figures) in biopsy or tumour rese…
View article: Patterns & Variations
Patterns & Variations Open
How can we better understand and explain the phenomena of architectural composition and perception? The aim of this research is to systematically and imaginatively (re)consider the conditions of architectural composition, whilst doing just…