1 code implementation • 16 May 2024 • Jack Breen, Katie Allen, Kieran Zucker, Lucy Godson, Nicolas M. Orsi, Nishant Ravikumar
Five-class classification performance was evaluated through five-fold cross-validation, and these cross-validation models were ensembled for evaluation on a hold-out test set and an external set from the Transcanadian study.
1 code implementation • 23 Nov 2023 • Jack Breen, Katie Allen, Kieran Zucker, Nicolas M. Orsi, Nishant Ravikumar
Artificial intelligence has found increasing use for ovarian cancer morphological subtyping from histopathology slides, but the optimal magnification for computational interpretation is unclear.
1 code implementation • 19 Oct 2023 • Jack Breen, Katie Allen, Kieran Zucker, Geoff Hall, Nishant Ravikumar, Nicolas M. Orsi
For some therapies, it is not possible to predict patients' responses, potentially exposing them to the adverse effects of treatment without any therapeutic benefit.
no code implementations • 5 Aug 2023 • Jack Breen, Kieran Zucker, Katie Allen, Nishant Ravikumar, Nicolas M. Orsi
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.
1 code implementation • 31 Mar 2023 • Jack Breen, Katie Allen, Kieran Zucker, Pratik Adusumilli, Andy Scarsbrook, Geoff Hall, Nicolas M. Orsi, Nishant Ravikumar
The inclusion criteria required that research evaluated AI on histopathology images for diagnostic or prognostic inferences in ovarian cancer.
1 code implementation • 17 Feb 2023 • Jack Breen, Katie Allen, Kieran Zucker, Geoff Hall, Nicolas M. Orsi, Nishant Ravikumar
Weakly-supervised classification of histopathology slides is a computationally intensive task, with a typical whole slide image (WSI) containing billions of pixels to process.
no code implementations • 6 Feb 2023 • Rachael Harkness, Alejandro F Frangi, Kieran Zucker, Nishant Ravikumar
We generate visual examples to show that our explainability method, when applied to the trained DirVAE, is able to highlight regions in CXR images that are clinically relevant to the class(es) of interest and additionally, can identify cases where classification relies on spurious feature correlations.
1 code implementation • 14 Sep 2021 • Rachael Harkness, Geoff Hall, Alejandro F Frangi, Nishant Ravikumar, Kieran Zucker
Model performance results have been exceptional when training and testing on open-source data, surpassing the reported capabilities of AI in pneumonia-detection prior to the COVID-19 outbreak.
1 code implementation • 1 Sep 2021 • Jack Breen, Kieran Zucker, Nicolas M. Orsi, Nishant Ravikumar
Two baseline mitosis detection models based on U-Net and RetinaNet were investigated in combination with the aforementioned domain adaptation methods.