James H. F. Rudd

According to our database, James H. F. Rudd authored at least 12 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Recent methodological advances in federated learning for healthcare.

Fan Zhang, Daniel Kreuter, Yichen Chen, Sören Dittmer, Samuel Tull, Tolou Shadbahr, Martijn Schut, Folkert W. Asselbergs, Sujoy Kar, Suthesh Sivapalaratnam, Sophie Williams, Mickey Koh, Yvonne Henskens, Bart de Wit, Umberto D'alessandro, Bubacarr Bah, Ousman Secka, Parashkev Nachev, Rajeev Gupta, Sara Trompeter, Nancy Boeckx, Christine van Laer, Gordon A. Awandare, Kwabena Sarpong, Lucas Amenga-Etego, Mathie Leers, Mirelle Huijskens, Samuel McDermott, Willem H. Ouwehand, Jacobus Preller, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb, Nicholas S. Gleadall, Michael Roberts

Patterns, 2024

Publisher Correction: The curious case of the test set AUROC.

Michael Roberts, Alon Hazan, Sören Dittmer, James H. F. Rudd, Carola-Bibiane Schönlieb

Nat. Mac. Intell., 2024

The curious case of the test set AUROC.

Michael Roberts, Alon Hazan, Sören Dittmer, James H. F. Rudd, Carola-Bibiane Schönlieb

Nat. Mac. Intell., 2024

Parameter choices in HaarPSI for IQA with medical images.

Clemens Karner, Janek Gröhl, Ian Selby, Judith Babar, Jake Beckford, Thomas R. Else, Timothy J. Sadler, Shahab Shahipasand, Arthikkaa Thavakumar, Michael Roberts, James H. F. Rudd, Carola-Bibiane Schönlieb, Jonathan R. Weir-McCall, Anna Breger

CoRR, 2024

2023

Navigating the development challenges in creating complex data systems.

Sören Dittmer, Michael Roberts, Julian D. Gilbey, Ander Biguri, Ian Selby, Anna Breger, Matthew Thorpe, Jonathan R. Weir-McCall, Effrossyni Gkrania-Klotsas, Anna Korhonen, Emily R. Jefferson, Georg Langs, Guang Yang, Helmut Prosch, Jan Stanczuk, Jing Tang, Judith Babar, Lorena Escudero Sanchez, Philip Teare, Mishal Patel, Marcel Wassin, Markus Holzer, Nicholas Walton, Pietro Lió, Tolou Shadbahr, Evis Sala, Jacobus Preller, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb

Nat. Mac. Intell., July, 2023

Recent Methodological Advances in Federated Learning for Healthcare.

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 S. Gleadall, Michael Roberts

CoRR, 2023

Reinterpreting survival analysis in the universal approximator age.

Sören Dittmer, Michael Roberts, Jacobus Preller, AIX-COVNET Collaboration, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb

CoRR, 2023

Dis-AE: Multi-domain & Multi-task Generalisation on Real-World Clinical Data.

Daniel Kreuter, Samuel Tull, Julian D. Gilbey, Jacobus Preller, BloodCounts Consortium, John A. D. Aston, James H. F. Rudd, Suthesh Sivapalaratnam, Carola-Bibiane Schönlieb, Nicholas S. Gleadall, Michael Roberts

CoRR, 2023

2022

Navigating the challenges in creating complex data systems: a development philosophy.

Sören Dittmer, Michael Roberts, Julian D. Gilbey, Ander Biguri, AIX-COVNET Collaboration, Jacobus Preller, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb

CoRR, 2022

Classification of datasets with imputed missing values: does imputation quality matter?

Tolou Shadbahr, Michael Roberts, Jan Stanczuk, Julian D. Gilbey, Philip Teare, Sören Dittmer, Matthew Thorpe, Ramón Viñas Torné, Evis Sala, Pietro Lió, Mishal Patel, AIX-COVNET Collaboration, James H. F. Rudd, Tuomas Mirtti, Antti Rannikko, John A. D. Aston, Jing Tang, Carola-Bibiane Schönlieb

CoRR, 2022

2021

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans.

Michael Roberts, Derek Driggs, Matthew Thorpe, Julian D. Gilbey, Michael Yeung, Stephan Ursprung, Angelica I. Avilés-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

Nat. Mach. Intell., 2021

2020

Machine learning for COVID-19 detection and prognostication using chest radiographs and CT scans: a systematic methodological review.

Michael Roberts, Derek Driggs, Matthew Thorpe, Julian D. Gilbey, Michael Yeung, Stephan Ursprung, Angelica I. Avilés-Rivero, Christian Etmann, Cathal McCague, Lucian Beer, Jonathan R. Weir-McCall, Zhongzhao Teng, James H. F. Rudd, Evis Sala, Carola-Bibiane Schönlieb

CoRR, 2020