Zobrazeno 1 - 10
of 625
pro vyhledávání: '"James, H. F."'
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:
Tolou Shadbahr, Michael Roberts, Jan Stanczuk, Julian Gilbey, Philip Teare, Sören Dittmer, Matthew Thorpe, Ramon Viñas Torné, Evis Sala, Pietro Lió, Mishal Patel, Jacobus Preller, AIX-COVNET Collaboration, James H. F. Rudd, Tuomas Mirtti, Antti Sakari Rannikko, John A. D. Aston, Jing Tang, Carola-Bibiane Schönlieb
Publikováno v:
Communications Medicine, Vol 3, Iss 1, Pp 1-15 (2023)
Abstract Background 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
Externí odkaz:
https://doaj.org/article/9d3ea5d2aba94675b450444e5c79243e
Autor:
Anna Breger, Ian Selby, Michael Roberts, Judith Babar, Effrossyni Gkrania-Klotsas, Jacobus Preller, Lorena Escudero Sánchez, AIX-COVNET Collaboration, James H. F. Rudd, John A. D. Aston, Jonathan R. Weir-McCall, Evis Sala, Carola-Bibiane Schönlieb
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-16 (2023)
Abstract The National COVID-19 Chest Imaging Database (NCCID) is a centralized UK database of thoracic imaging and corresponding clinical data. It is made available by the National Health Service Artificial Intelligence (NHS AI) Lab to support the de
Externí odkaz:
https://doaj.org/article/9878da5de9d34045a08b088c7c1813d9
Autor:
Roberts, Michael, Driggs, Derek, Thorpe, Matthew, Gilbey, Julian, Yeung, Michael, Ursprung, Stephan, Aviles-Rivero, Angelica I., Etmann, Christian, McCague, Cathal, Beer, Lucian, Weir-McCall, Jonathan R., Teng, Zhongzhao, Gkrania-Klotsas, Effrossyni, Rudd, James H. F., Sala, Evis, Schönlieb, Carola-Bibiane
Publikováno v:
Nature Machine Intelligence 3, 199-217 (2021)
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. Many articles have been published in 2020 describing new m
Externí odkaz:
http://arxiv.org/abs/2008.06388