Deep learning-Autoencoders

Melle Vessies; Rutger van de Leur; Philippe Wouters; René van Es

doi:10.1007/978-3-031-36678-9_13

Deep learning-Autoencoders

Melle Vessies, Rutger van de Leur, Philippe Wouters, René van Es^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

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 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.

Original language	English
Title of host publication	Clinical Applications of Artificial Intelligence in Real-World Data
Editors	Folkert W. Asselbergs, Spiros Denaxas, Daniel L. Oberski, Jason H. Moore
Publisher	Springer
Pages	203-220
Number of pages	18
Edition	1
ISBN (Electronic)	9783031366789
ISBN (Print)	9783031366772
DOIs	https://doi.org/10.1007/978-3-031-36678-9_13
Publication status	Published - 5 Nov 2023

Keywords

Anomaly detection
Auto-encoder
Deep learning
Denoising
Dimension reduction
Disentanglement
ECG
Explainable AI
Variational auto-encoder

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10.1007/978-3-031-36678-9_13

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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 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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Deep learning-Autoencoders. / Vessies, Melle; van de Leur, Rutger; Wouters, Philippe et al.
Clinical Applications of Artificial Intelligence in Real-World Data. ed. / Folkert W. Asselbergs; Spiros Denaxas; Daniel L. Oberski; Jason H. Moore. 1. ed. Springer, 2023. p. 203-220.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

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KW - Anomaly detection

KW - Auto-encoder

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KW - Denoising

KW - Dimension reduction

KW - Disentanglement

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KW - Explainable AI

KW - Variational auto-encoder

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Deep learning-Autoencoders

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Keywords

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