Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Zucker, Kieran"'
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 spatial relatio
Externí odkaz:
http://arxiv.org/abs/2407.18105
Autor:
Breen, Jack, Allen, Katie, Zucker, Kieran, Godson, Lucy, Orsi, Nicolas M., Ravikumar, Nishant
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, but analy
Externí odkaz:
http://arxiv.org/abs/2405.09990
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 cytological inform
Externí odkaz:
http://arxiv.org/abs/2311.13956
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 therapeutic benefit.
Externí odkaz:
http://arxiv.org/abs/2310.12866
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 roadblocks to curr
Externí odkaz:
http://arxiv.org/abs/2308.02851
Autor:
Breen, Jack, Allen, Katie, Zucker, Kieran, Adusumilli, Pratik, Scarsbrook, Andy, Hall, Geoff, Orsi, Nicolas M., Ravikumar, Nishant
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 Science, CENTRAL,
Externí odkaz:
http://arxiv.org/abs/2303.18005
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 Instance Lea
Externí odkaz:
http://arxiv.org/abs/2302.08867
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 is that distributional sparsity, as facilitated by the Dirichlet prior,
Externí odkaz:
http://arxiv.org/abs/2302.02979
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 relies heavily on public data comprising numerous data sources. Model
Externí odkaz:
http://arxiv.org/abs/2109.08020
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 resection speci
Externí odkaz:
http://arxiv.org/abs/2109.00869