1 code implementation • 19 Dec 2023 • Michael Roberts, Alon Hazan, Sören Dittmer, James H. F. Rudd, Carola-Bibiane Schönlieb
Whilst the size and complexity of ML models have rapidly and significantly increased over the past decade, the methods for assessing their performance have not kept pace.
no code implementations • 4 Oct 2023 • Fan Zhang, Daniel Kreuter, Yichen Chen, Sören Dittmer, Samuel Tull, Tolou Shadbahr, BloodCounts! Collaboration, Jacobus Preller, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb, Nicholas Gleadall, Michael Roberts
We give detailed recommendations to help improve the quality of the methodology development for federated learning in healthcare.
1 code implementation • 25 Jul 2023 • Sören Dittmer, Michael Roberts, Jacobus Preller, AIX COVNET, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb
We aim to provide the tools needed to fully harness the potential of survival analysis in deep learning.
1 code implementation • 15 Jun 2023 • Daniel Kreuter, Samuel Tull, Julian Gilbey, Jacobus Preller, BloodCounts! Consortium, John A. D. Aston, James H. F. Rudd, Suthesh Sivapalaratnam, Carola-Bibiane Schönlieb, Nicholas Gleadall, Michael Roberts
Clinical data is often affected by clinically irrelevant factors such as discrepancies between measurement devices or differing processing methods between sites.
no code implementations • 21 Oct 2022 • Sören Dittmer, Michael Roberts, Julian Gilbey, Ander Biguri, AIX-COVNET Collaboration, Jacobus Preller, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb
In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy and effective data science system (DSS) is getting harder.
no code implementations • 16 Jun 2022 • Tolou Shadbahr, Michael Roberts, Jan Stanczuk, Julian Gilbey, Philip Teare, Sören Dittmer, Matthew Thorpe, Ramon Vinas Torne, Evis Sala, Pietro Lio, Mishal Patel, AIX-COVNET Collaboration, James H. F. Rudd, Tuomas Mirtti, Antti Rannikko, John A. D. Aston, Jing Tang, Carola-Bibiane Schönlieb
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial.
no code implementations • 14 Aug 2020 • Michael Roberts, Derek Driggs, Matthew Thorpe, Julian Gilbey, Michael Yeung, Stephan Ursprung, Angelica I. Aviles-Rivero, Christian Etmann, Cathal McCague, Lucian Beer, Jonathan R. Weir-McCall, Zhongzhao Teng, Effrossyni Gkrania-Klotsas, James H. F. Rudd, Evis Sala, Carola-Bibiane Schönlieb
Machine learning methods offer great promise for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and computed tomography (CT) images.