Zobrazeno 1 - 10
of 1 079
pro vyhledávání: '"Rudd, James"'
Autor:
Karner, Clemens, Gröhl, Janek, Selby, Ian, Babar, Judith, Beckford, Jake, Else, Thomas R, Sadler, Timothy J, Shahipasand, Shahab, Thavakumar, Arthikkaa, Roberts, Michael, Rudd, James H. F., Schönlieb, Carola-Bibiane, Weir-McCall, Jonathan R, Breger, Anna
When developing machine learning models, image quality assessment (IQA) measures are a crucial component for evaluation. However, commonly used IQA measures have been primarily developed and optimized for natural images. In many specialized settings,
Externí odkaz:
http://arxiv.org/abs/2410.24098
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. In particular, among the many potential performance metrics, the ML community s
Externí odkaz:
http://arxiv.org/abs/2312.16188
Autor:
Zhang, Fan, Kreuter, Daniel, Chen, Yichen, Dittmer, Sören, Tull, Samuel, Shadbahr, Tolou, Collaboration, BloodCounts!, Preller, Jacobus, Rudd, James H. F., Aston, John A. D., Schönlieb, Carola-Bibiane, Gleadall, Nicholas, Roberts, Michael
For healthcare datasets, it is often not possible to combine data samples from multiple sites due to ethical, privacy or logistical concerns. Federated learning allows for the utilisation of powerful machine learning algorithms without requiring the
Externí odkaz:
http://arxiv.org/abs/2310.02874
Autor:
Dittmer, Sören, Roberts, Michael, Preller, Jacobus, COVNET, AIX, Rudd, James H. F., Aston, John A. D., Schönlieb, Carola-Bibiane
Survival analysis is an integral part of the statistical toolbox. However, while most domains of classical statistics have embraced deep learning, survival analysis only recently gained some minor attention from the deep learning community. This rece
Externí odkaz:
http://arxiv.org/abs/2307.13579
Autor:
Kreuter, Daniel, Tull, Samuel, Gilbey, Julian, Preller, Jacobus, Consortium, BloodCounts!, Aston, John A. D., Rudd, James H. F., Sivapalaratnam, Suthesh, Schönlieb, Carola-Bibiane, Gleadall, Nicholas, Roberts, Michael
Clinical data is often affected by clinically irrelevant factors such as discrepancies between measurement devices or differing processing methods between sites. In the field of machine learning (ML), these factors are known as domains and the distri
Externí odkaz:
http://arxiv.org/abs/2306.09177
Autor:
Dittmer, Sören, Roberts, Michael, Gilbey, Julian, Biguri, Ander, Collaboration, AIX-COVNET, Preller, Jacobus, Rudd, James H. F., Aston, John A. D., Schönlieb, Carola-Bibiane
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. Perverse inc
Externí odkaz:
http://arxiv.org/abs/2210.13191
Autor:
Shadbahr, Tolou, Roberts, Michael, Stanczuk, Jan, Gilbey, Julian, Teare, Philip, Dittmer, Sören, Thorpe, Matthew, Torne, Ramon Vinas, Sala, Evis, Lio, Pietro, Patel, Mishal, Collaboration, AIX-COVNET, Rudd, James H. F., Mirtti, Tuomas, Rannikko, Antti, Aston, John A. D., Tang, Jing, Schönlieb, Carola-Bibiane
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by
Externí odkaz:
http://arxiv.org/abs/2206.08478
Autor:
Le, Elizabeth P.V., Wong, Mark Y.Z., Rundo, Leonardo, Tarkin, Jason M., Evans, Nicholas R., Weir-McCall, Jonathan R., Chowdhury, Mohammed M., Coughlin, Patrick A., Pavey, Holly, Zaccagna, Fulvio, Wall, Chris, Sriranjan, Rouchelle, Corovic, Andrej, Huang, Yuan, Warburton, Elizabeth A., Sala, Evis, Roberts, Michael, Schönlieb, Carola-Bibiane, Rudd, James H.F.
Publikováno v:
In European Journal of Radiology Open December 2024 13
Autor:
Wang, Kang-Ling, Balmforth, Craig, Meah, Mohammed N., Daghem, Marwa, Moss, Alastair J., Tzolos, Evangelos, Kwiecinski, Jacek, Molek-Dziadosz, Patrycja, Craig, Neil, Bularga, Anda, Adamson, Philip D., Dawson, Dana K., Arumugam, Parthiban, Sabharwal, Nikant K., Greenwood, John P., Townend, Jonathan N., Calvert, Patrick A., Rudd, James H.F., Verjans, Johan W., Berman, Daniel S., Slomka, Piotr J., Dey, Damini, Mills, Nicholas L., van Beek, Edwin J.R., Williams, Michelle C., Dweck, Marc R., Newby, David E.
Publikováno v:
In Journal of the American College of Cardiology 4 June 2024 83(22):2135-2144