Reference : Model bias and its impact on computer-aided diagnosis: A data-centric approach
Scientific congresses, symposiums and conference proceedings : Poster
Engineering, computing & technology : Computer science
Systems Biomedicine; Computational Sciences
http://hdl.handle.net/10993/47876
Model bias and its impact on computer-aided diagnosis: A data-centric approach
English
Garcia Santa Cruz, Beatriz mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Bossa, Matias Nicolas [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Interventional Neuroscience >]
Sölter, Jan [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Interventional Neuroscience >]
Husch, Andreas mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Interventional Neuroscience >]
Hertel, Frank [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > >]
Aug-2021
Yes
No
International
2021 MLSS
from 2-08-2021 to 20-8-2021
University of Taiwan
Taiwan (Online)
[en] ML-Ops ; model bias ; Fairness in AI ; Covid-19 ; applied-ML healthcare
[en] Machine learning and data-driven solutions open exciting opportunities in many disciplines including healthcare. The recent transition to this technology into real clinical settings brings new challenges. Such problems derive from several factors, including their dataset origin, composition and description, hampering their fairness and secure application. Considering the potential impact of incorrect predictions in applied-ML healthcare research is urgent.

Undetected bias induced by inappropriate use of datasets and improper consideration of confounders prevents the translation of prediction models into clinical practice. Therefore, in this work, the use of available systematic tools to assess the risk of bias in models is employed as the first step to explore robust solutions for better dataset choice, dataset merge and design of the training and validation step during the ML development pipeline.
Luxembourg Centre for Systems Biomedicine (LCSB)
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/47876
10.5281/zenodo.5205671

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