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Outlier Detection using Self-Organizing Maps for Automated Blood Cell Analysis. (arXiv:2208.08834v1 [eess.IV])
Aug. 19, 2022, 1:10 a.m. | Stefan Röhrl, Alice Hein, Lucie Huang, Dominik Heim, Christian Klenk, Manuel Lengl, Martin Knopp, Nawal Hafez, Oliver Hayden, Klaus Diepold
cs.LG updates on arXiv.org arxiv.org
The quality of datasets plays a crucial role in the successful training and
deployment of deep learning models. Especially in the medical field, where
system performance may impact the health of patients, clean datasets are a
safety requirement for reliable predictions. Therefore, outlier detection is an
essential process when building autonomous clinical decision systems. In this
work, we assess the suitability of Self-Organizing Maps for outlier detection
specifically on a medical dataset containing quantitative phase images of white
blood cells. …
More from arxiv.org / cs.LG updates on arXiv.org
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