Deep learning-Autoencoders
Vessies, Melle; van de Leur, Rutger; Wouters, Philippe; van Es, René
(2023) Asselbergs, Folkert W., Denaxas, Spiros, Oberski, Daniel L., Moore, Jason H. (eds.), Clinical Applications of Artificial Intelligence in Real-World Data, pp. 203 - 220
(Part of book)
Abstract
Auto-encoders and their variational counterparts form a family of (deep) neural networks that serve a wide range of applications in medical research and clinical practice. In this chapter we provide a comprehensive overview of how auto-encoders work and how they can be used to improve medical research. We elaborate on
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various topics such as dimension reduction, denoising auto-encoders, auto-encoders used for anomaly detection and the applications of representations of data created using auto-encoders. Secondly, we touch upon the subject of variational auto-encoders, explaining their design and training process. We end the chapter with small scale examples of auto-encoders applied to the MNIST dataset and a recent example of an application of a (disentangled) variational auto-encoder applied to ECG-data.
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Keywords: Anomaly detection, Auto-encoder, Deep learning, Denoising, Dimension reduction, Disentanglement, ECG, Explainable AI, Variational auto-encoder, General Medicine, General Health Professions, General Nursing, General Biochemistry,Genetics and Molecular Biology, General Agricultural and Biological Sciences, General Computer Science
ISBN: 9783031366772
9783031366789
Publisher: Springer
Note: Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. All rights reserved.
(Peer reviewed)