Assessing gender bias in medical and scientific masked language models with StereoSet

no code implementations • 15 Nov 2021 • Robert Robinson

Pre-training on large quantities of text also has the potential to transparently embed the cultural and social biases found in the source text into the MLM system.

Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects

no code implementations • 10 Oct 2019 • Ben Glocker, Robert Robinson, Daniel C. Castro, Qi Dou, Ender Konukoglu

This is an empirical study to investigate the impact of scanner effects when using machine learning on multi-site neuroimaging data.

BIG-bench Machine Learning

Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study

no code implementations • 27 Jan 2019 • Robert Robinson, Vanya V. Valindria, Wenjia Bai, Ozan Oktay, Bernhard Kainz, Hideaki Suzuki, Mihir M. Sanghvi, Nay Aung, Jos$é$ Miguel Paiva, Filip Zemrak, Kenneth Fung, Elena Lukaschuk, Aaron M. Lee, Valentina Carapella, Young Jin Kim, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Chris Page, Paul M. Matthews, Daniel Rueckert, Ben Glocker

Methods: To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis.

General Classification Image Segmentation +2

Real-time Prediction of Segmentation Quality

no code implementations • 16 Jun 2018 • Robert Robinson, Ozan Oktay, Wenjia Bai, Vanya Valindria, Mihir Sanghvi, Nay Aung, José Paiva, Filip Zemrak, Kenneth Fung, Elena Lukaschuk, Aaron Lee, Valentina Carapella, Young Jin Kim, Bernhard Kainz, Stefan Piechnik, Stefan Neubauer, Steffen Petersen, Chris Page, Daniel Rueckert, Ben Glocker

Recent advances in deep learning based image segmentation methods have enabled real-time performance with human-level accuracy.

Binary Classification General Classification +3