A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions

Deep learning algorithms trained on data streamed temporally from different clinical sites and from a multitude of physiological sensors are generally affected by a degradation in performance. To mitigate this, the authors propose a continual learning strategy that employs a replay buffer.

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Bibliographic Details
Main Authors: Dani Kiyasseh, Tingting Zhu, David Clifton
Format: article
Language:EN
Published: Nature Portfolio 2021
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Online Access:https://doaj.org/article/5943e24bee1a4eb1be3eaa04425d47c2
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Description
Summary:Deep learning algorithms trained on data streamed temporally from different clinical sites and from a multitude of physiological sensors are generally affected by a degradation in performance. To mitigate this, the authors propose a continual learning strategy that employs a replay buffer.