Reply to Letter to the Editor on “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”

Detalles Bibliográficos
Autores principales: Pot, Mirjam, Prainsack, Barbara
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Opinion
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8564595/
https://www.ncbi.nlm.nih.gov/pubmed/34731337
http://dx.doi.org/10.1186/s13244-021-01088-1
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author Pot, Mirjam
Prainsack, Barbara
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Prainsack, Barbara
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spelling pubmed-85645952021-11-03 Reply to Letter to the Editor on “Not all biases are bad: equitable and inequitable biases in machine learning and radiology” Pot, Mirjam Prainsack, Barbara Insights Imaging Opinion Springer International Publishing 2021-11-03 /pmc/articles/PMC8564595/ /pubmed/34731337 http://dx.doi.org/10.1186/s13244-021-01088-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Opinion
Pot, Mirjam
Prainsack, Barbara
Reply to Letter to the Editor on “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”
title Reply to Letter to the Editor on “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”
title_full Reply to Letter to the Editor on “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”
title_fullStr Reply to Letter to the Editor on “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”
title_full_unstemmed Reply to Letter to the Editor on “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”
title_short Reply to Letter to the Editor on “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”
title_sort reply to letter to the editor on “not all biases are bad: equitable and inequitable biases in machine learning and radiology”
topic Opinion
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8564595/
https://www.ncbi.nlm.nih.gov/pubmed/34731337
http://dx.doi.org/10.1186/s13244-021-01088-1
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AT prainsackbarbara replytolettertotheeditoronnotallbiasesarebadequitableandinequitablebiasesinmachinelearningandradiology

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