Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging

In clinical practice, the continuous progress of image acquisition technology or diagnostic procedures and evolving imaging protocols hamper the utility of machine learning, as prediction accuracy on new data deteriorates. Here, the authors propose a continual learning approach to deal with such dom...

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Autores principales:	Matthias Perkonigg, Johannes Hofmanninger, Christian J. Herold, James A. Brink, Oleg Pianykh, Helmut Prosch, Georg Langs
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/f9fc7f5d825149758843a206d878d863
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spelling	oai:doaj.org-article:f9fc7f5d825149758843a206d878d8632021-12-02T18:51:30ZDynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging10.1038/s41467-021-25858-z2041-1723https://doaj.org/article/f9fc7f5d825149758843a206d878d8632021-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25858-zhttps://doaj.org/toc/2041-1723In clinical practice, the continuous progress of image acquisition technology or diagnostic procedures and evolving imaging protocols hamper the utility of machine learning, as prediction accuracy on new data deteriorates. Here, the authors propose a continual learning approach to deal with such domain shifts occurring at unknown time points.Matthias PerkoniggJohannes HofmanningerChristian J. HeroldJames A. BrinkOleg PianykhHelmut ProschGeorg LangsNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Matthias Perkonigg Johannes Hofmanninger Christian J. Herold James A. Brink Oleg Pianykh Helmut Prosch Georg Langs Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging
description	In clinical practice, the continuous progress of image acquisition technology or diagnostic procedures and evolving imaging protocols hamper the utility of machine learning, as prediction accuracy on new data deteriorates. Here, the authors propose a continual learning approach to deal with such domain shifts occurring at unknown time points.
format	article
author	Matthias Perkonigg Johannes Hofmanninger Christian J. Herold James A. Brink Oleg Pianykh Helmut Prosch Georg Langs
author_facet	Matthias Perkonigg Johannes Hofmanninger Christian J. Herold James A. Brink Oleg Pianykh Helmut Prosch Georg Langs
author_sort	Matthias Perkonigg
title	Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging
title_short	Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging
title_full	Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging
title_fullStr	Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging
title_full_unstemmed	Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging
title_sort	dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f9fc7f5d825149758843a206d878d863
work_keys_str_mv	AT matthiasperkonigg dynamicmemorytoalleviatecatastrophicforgettingincontinuallearningwithmedicalimaging AT johanneshofmanninger dynamicmemorytoalleviatecatastrophicforgettingincontinuallearningwithmedicalimaging AT christianjherold dynamicmemorytoalleviatecatastrophicforgettingincontinuallearningwithmedicalimaging AT jamesabrink dynamicmemorytoalleviatecatastrophicforgettingincontinuallearningwithmedicalimaging AT olegpianykh dynamicmemorytoalleviatecatastrophicforgettingincontinuallearningwithmedicalimaging AT helmutprosch dynamicmemorytoalleviatecatastrophicforgettingincontinuallearningwithmedicalimaging AT georglangs dynamicmemorytoalleviatecatastrophicforgettingincontinuallearningwithmedicalimaging
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Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging

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