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pro vyhledávání: '"Dawood, Tareen"'
Improving calibration performance in deep learning (DL) classification models is important when planning the use of DL in a decision-support setting. In such a scenario, a confident wrong prediction could lead to a lack of trust and/or harm in a high
Externí odkaz:
http://arxiv.org/abs/2405.06487
Autor:
Dawood, Tareen, Chen, Chen, Sidhua, Baldeep S., Ruijsink, Bram, Goulda, Justin, Porter, Bradley, Elliott, Mark K., Mehta, Vishal, Rinaldi, Christopher A., Puyol-Anton, Esther, Razavi, Reza, King, Andrew P.
Quantifying uncertainty of predictions has been identified as one way to develop more trustworthy artificial intelligence (AI) models beyond conventional reporting of performance metrics. When considering their role in a clinical decision support set
Externí odkaz:
http://arxiv.org/abs/2308.15141
In terms of accuracy, deep learning (DL) models have had considerable success in classification problems for medical imaging applications. However, it is well-known that the outputs of such models, which typically utilise the SoftMax function in the
Externí odkaz:
http://arxiv.org/abs/2301.13296
Autor:
Dawood, Tareen
The varying clinical presentation of Hodgkin's lymphoma (HL) poses a diagnostic challenge in South Africa, as the clinical picture of this lymphoma overlaps with prevalent comorbidities such as tuberculosis (TB) and the Human Immuno-Deficiency Virus
Externí odkaz:
http://hdl.handle.net/11427/35492
Autor:
Dawood, Tareen, Chen, Chen, Andlauer, Robin, Sidhu, Baldeep S., Ruijsink, Bram, Gould, Justin, Porter, Bradley, Elliott, Mark, Mehta, Vishal, Rinaldi, C. Aldo, Puyol-Antón, Esther, Razavi, Reza, King, Andrew P.
Evaluation of predictive deep learning (DL) models beyond conventional performance metrics has become increasingly important for applications in sensitive environments like healthcare. Such models might have the capability to encode and analyse large
Externí odkaz:
http://arxiv.org/abs/2109.10641
Akademický článek
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Autor:
Simba, Kudakwashe, Mohamed, Zainab, Opie, Jessica J., Andera, Lillian F., Brown, Karryn, Oosthuizen, Jenna, Antel, Katherine, Dawood, Tareen, Van der Vyfer, Lydia, Du Toit, Cecile, Louw, Vernon J., Verburgh, Estelle
Publikováno v:
Leukemia & Lymphoma; Mar2023, Vol. 64 Issue 3, p613-620, 8p
Autor:
Dawood T; School of Biomedical Engineering & Imaging Sciences, King's College London, UK., Chan E; School of Biomedical Engineering & Imaging Sciences, King's College London, UK., Razavi R; School of Biomedical Engineering & Imaging Sciences, King's College London, UK., King AP; School of Biomedical Engineering & Imaging Sciences, King's College London, UK., Puyol-Antón E; School of Biomedical Engineering & Imaging Sciences, King's College London, UK.
Publikováno v:
Proceedings. IEEE International Symposium on Biomedical Imaging [Proc IEEE Int Symp Biomed Imaging] 2023 Apr 18; Vol. 34, pp. 1-5.