Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Mélanie Bernhardt"'
Publikováno v:
EBioMedicine, Vol 89, Iss , Pp 104467- (2023)
Summary: Background: It has been rightfully emphasized that the use of AI for clinical decision making could amplify health disparities. An algorithm may encode protected characteristics, and then use this information for making predictions due to un
Externí odkaz:
https://doaj.org/article/a3d3aada74b84787b0ef22431a38074c
Autor:
Mélanie Bernhardt, Daniel C. Castro, Ryutaro Tanno, Anton Schwaighofer, Kerem C. Tezcan, Miguel Monteiro, Shruthi Bannur, Matthew P. Lungren, Aditya Nori, Ben Glocker, Javier Alvarez-Valle, Ozan Oktay
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-11 (2022)
High quality labels are important for model performance, evaluation and selection in medical imaging. As manual labelling is time-consuming and costly, the authors explore and benchmark various resource-effective methods for improving dataset quality
Externí odkaz:
https://doaj.org/article/70d06a2166804fea9694afa4ba328c23
Autor:
Mélanie Bernhardt, Daniel C. Castro, Ryutaro Tanno, Anton Schwaighofer, Kerem C. Tezcan, Miguel Monteiro, Shruthi Bannur, Matthew P. Lungren, Aditya Nori, Ben Glocker, Javier Alvarez-Valle, Ozan Oktay
Publikováno v:
Nature communications. 13(1)
Imperfections in data annotation, known as label noise, are detrimental to the training of machine learning models and have an often-overlooked confounding effect on the assessment of model performance. Nevertheless, employing experts to remove label
Publikováno v:
Nature Medicine
An increasing number of reports raise concerns about the risk that machine learning algorithms could amplify health disparities due to biases embedded in the training data. Seyyed-Kalantari et al. find that models trained on three chest X-ray dataset