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Deep Learning—Autoencoders

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Clinical Applications of Artificial Intelligence in Real-World Data

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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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References

  1. Hinton GE, Zemel RS. Autoencoders, minimum description length and Helmholtz free energy. p. 8.

    Google Scholar 

  2. Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science. 2006;313(5786):504–7. https://doi.org/10.1126/science.1127647.

    Article  MathSciNet  MATH  Google Scholar 

  3. Using autoencoders for mammogram compression. PubMed. https://pubmed.ncbi.nlm.nih.gov/20703586/. Accessed 31 Jan 2022.

  4. Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278–324. https://doi.org/10.1109/5.726791.

    Article  Google Scholar 

  5. Baldi P, Hornik K. Neural networks and principal component analysis: learning from examples without local minima. Neural Netw. 1989;2(1):53–8. https://doi.org/10.1016/0893-6080(89)90014-2.

    Article  Google Scholar 

  6. Mohd Sagheer SV, George SN. A review on medical image denoising algorithms. Biomed Signal Process Control 2020;61:102036. https://doi.org/10.1016/j.bspc.2020.102036.

  7. Gondara L. Medical image denoising using convolutional denoising autoencoders. In: 2016 IEEE 16th international conference on data mining workshops (ICDMW), Barcelona, Spain, Dec 2016, pp 241–46. https://doi.org/10.1109/ICDMW.2016.0041

  8. Lu X, Tsao Y, Matsuda S, Hori C. Speech enhancement based on deep denoising auto-encoder. In: Proceedings of interspeech, Jan 2013. p. 436–40.

    Google Scholar 

  9. Jifara W, Jiang F, Rho S, Cheng M, Liu S. Medical image denoising using convolutional neural network: a residual learning approach. J Supercomput. 2019;75(2):704–18. https://doi.org/10.1007/s11227-017-2080-0.

    Article  Google Scholar 

  10. Nawarathne T et al. Comprehensive study on denoising of medical images utilizing neural network based auto-encoder. Feb 2021. arXiv:2102.01903 [eess]. Accessed 30 Jan 2022. Available http://arxiv.org/abs/2102.01903

  11. Shvetsova N, Bakker B, Fedulova I, Schulz H, Dylov DV. Anomaly detection in medical imaging with deep perceptual autoencoders. IEEE Access. 2021;9:118571–83. https://doi.org/10.1109/ACCESS.2021.3107163.

    Article  Google Scholar 

  12. Wei Q, Shi B, Lo JY, Carin L, Ren Y, Hou, R. Anomaly detection for medical images based on a one-class classification. In: Medical imaging 2018: computer-aided diagnosis, Houston, United States, Feb 2018. p 57. https://doi.org/10.1117/12.2293408

  13. Hinton GE, Krizhevsky A, Wang SD. Transforming auto-encoders. In: Artificial neural networks and machine learning—ICANN 2011, Berlin, Heidelberg, 2011. p. 44–51.

    Google Scholar 

  14. Fabius O, van Amersfoort JR. Variational Recurrent auto-encoders. arXiv:1412.6581 [cs, stat], Jun 2015. Accessed 31 Jan 2022. Available http://arxiv.org/abs/1412.6581

  15. Vessies MB et al. Interpretable ECG classification via a query-based latent space traversal (qLST). arXiv:2111.07386 [cs, eess], Nov 2021, Accessed 31 Jan 2022. Available http://arxiv.org/abs/2111.07386

  16. Kingma DP, Welling M. Auto-encoding variational Bayes. arXiv:1312.6114 [cs, stat], May 2014. Accessed 30 Jan 2022. Available http://arxiv.org/abs/1312.6114

  17. Higgins I et al.: β-VAE: learning basic visual concepts with a constrained variational framework. 2017. p. 22.

    Google Scholar 

  18. Van Steenkiste T, Deschrijver D, Dhaene T. Generating an explainable ECG beat space with variational auto-encoders. arXiv:1911.04898 [cs, eess, stat], Nov 2019. Accessed 30 Jan 2022. Available http://arxiv.org/abs/1911.04898

  19. van de Leur RR et al. Inherently explainable deep neural network-based interpretation of electrocardiograms using variational auto-encoders. Cardiovasc Med. 2022;preprint. https://doi.org/10.1101/2022.01.04.22268759.

  20. Kim J-Y, Cho S. BasisVAE: orthogonal latent space for deep disentangled representation. Sep 2019. Accessed 30 Jan 2022. Available https://openreview.net/forum?id=S1gEFkrtvH

  21. Chen RTQ, Li X, Grosse R, Duvenaud D. Isolating sources of disentanglement in variational autoencoders. arXiv:1802.04942 [cs, stat], Apr 2019. Accessed 30 Jan 30 2022. Available http://arxiv.org/abs/1802.04942

  22. Asperti A, Trentin M. Balancing reconstruction error and Kullback-Leibler divergence in variational autoencoders. arXiv:2002.07514 [cs], Feb 2020. Accessed 30 Jan 2022. Available http://arxiv.org/abs/2002.07514

  23. Lucas J, Tucker G, Grosse R, Norouzi M. Understanding posterior collapse in generative latent variable models. 2019. p. 16.

    Google Scholar 

  24. Shao H et al. Control VAE: controllable variational autoencoder. arXiv:2004.05988 [cs, stat], Jun 2020. Accessed 30 Jan 2022. Available: http://arxiv.org/abs/2004.05988

  25. Guo X, Gichoya JW, Purkayastha S, Banerjee I. CVAD: a generic medical anomaly detector based on Cascade VAE. arXiv:2110.15811 [cs, eess], Jan 2022. Accessed 30 Jan 2022. Available http://arxiv.org/abs/2110.15811

  26. Cakmak AS et al. Using convolutional variational autoencoders to predict post-trauma health outcomes from actigraphy data. arXiv:2011.07406 [cs, eess], Nov 2020. Accessed 25 Jan 2022. Available http://arxiv.org/abs/2011.07406

  27. Ministerie van Volksgezondheid WS. Guideline for high-quality diagnostic and prognostic applications of AI in healthcare—Publicatie - Data voor gezondheid. 28 Dec 2021. https://www.datavoorgezondheid.nl/documenten/publicaties/2021/12/17/guideline-for-high-quality-diagnostic-and-prognostic-applications-of-ai-in-healthcare. Accessed 01 Feb 2022.

  28. Wang Y, Yao H, Zhao S. Auto-encoder based dimensionality reduction. Neurocomputing. 2016;184:232–42. https://doi.org/10.1016/j.neucom.2015.08.104.

    Article  Google Scholar 

  29. He K, Zhang X, Ren S, Sun J. Identity mappings in deep residual networks. In: Computer vision—ECCV, Cham. 2016. p. 630–45. https://doi.org/10.1007/978-3-319-46493-0_38.

  30. Chen H, et al. Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans Med Imaging. 2017;36(12):2524–35. https://doi.org/10.1109/TMI.2017.2715284.

    Article  Google Scholar 

  31. van de Leur RR, et al. Automatic triage of 12-lead ECGs using deep convolutional neural networks. J Am Heart Assoc. 2020;9(10): e015138. https://doi.org/10.1161/JAHA.119.015138.

    Article  Google Scholar 

  32. Attia ZI, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019;25(1):70–4. https://doi.org/10.1038/s41591-018-0240-2.

    Article  Google Scholar 

  33. van de Leur RR, et al. Discovering and Visualizing disease-specific electrocardiogram features using deep learning: proof-of-concept in phospholamban gene mutation carriers. Circ Arrhythm Electrophysiol. 2021;14(2): e009056. https://doi.org/10.1161/CIRCEP.120.009056.

    Article  Google Scholar 

  34. Ribeiro AH, et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 2020;11(1):1760. https://doi.org/10.1038/s41467-020-15432-4.

    Article  Google Scholar 

  35. Goodman B, Flaxman S. European Union regulations on algorithmic decision-making and a ‘right to explanation.’ AIMag. 2017;38(3):50–7. https://doi.org/10.1609/aimag.v38i3.2741.

    Article  Google Scholar 

  36. Markus AF, Kors JA, Rijnbeek PR. The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies. J Biomed Inform. 2021;113: 103655. https://doi.org/10.1016/j.jbi.2020.103655.

    Article  Google Scholar 

  37. Hughes JW, et al. Performance of a convolutional neural network and explainability technique for 12-lead electrocardiogram interpretation. JAMA Cardiol. 2021;6(11):1285–95. https://doi.org/10.1001/jamacardio.2021.2746.

    Article  Google Scholar 

  38. Hooker S, Erhan D, Kindermans P-J, Kim B. A benchmark for interpretability methods in deep neural networks. arXiv:1806.10758 [cs, stat], Nov 2019. Accessed 31 Jan 2022. Available http://arxiv.org/abs/1806.10758

  39. Adebayo J, Gilmer J, Muelly M, Goodfellow I, Hardt M, Kim B. Sanity checks for saliency maps. arXiv:1810.03292 [cs, stat], Nov 2020, Accessed 31 Jan 2022. Available http://arxiv.org/abs/1810.03292

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Authors and Affiliations

  1. University Medical Center Utrecht, Utrecht, Netherlands

    Melle Vessies, Rutger van de Leur, Philippe Wouters & René van Es

Authors
  1. Melle Vessies
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  2. Rutger van de Leur
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  3. Philippe Wouters
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  4. René van Es
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Corresponding author

Correspondence to René van Es .

Editor information

Editors and Affiliations

  1. Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands

    Folkert W. Asselbergs

  2. Institute of Health Informatics, University College London, London, UK

    Spiros Denaxas

  3. Department of Data Science and Biostatistics, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands

    Daniel L. Oberski

  4. Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Jason H. Moore

Glossary

Activation function

In neural networks, (non-linear) activation functions are used at the output of neurons to convert the input to an ‘active’ or ‘not active’ state. An activation function can be a simple linear or sigmoid function or have more complex arbitrary forms. The Rectified Linear Unit (ReLU) function is currently the most popular choice.

Back propagation

Is a widely used technique in the field of machine learning that is used during the training of a neural network. The technique is used to update the weights of the neural network based on the calculated loss, effectively allowing it to ‘learn’.

(mini-) Batch

A small set of data samples that is fed through the network at once during training. A too small batch size may lead to instability while a too large batch size may lead to depletion of computer resources.

Convolution

Common building block of various neural networks. Convolutional neural networks can be considered the current ‘state of the art’ of neural networks applied to various data sources. Convolutional layers in a neural network a apply a learned filter to the input data which improves the ability of neural networks to comprehend spatial structures. Convolutions can be applied in 1 dimensional (signal/timeseries data) and 2 dimensional (images) forms.

Decoder

Part of the (variational) auto-encoder that decodes the given latent vector into a reconstruction of the original data

Dimension

The dimension of data is the size of the dataset or vector, for a grayscale image this is the height × the width in pixels (e.g. 28 × 28), for an RGB-color image, a third dimension of size 3 is added (e.g. (28 × 28 × 3)

Encoder

Part of the (variational) auto-encoder that encodes the provided data into the latent vector

Explainability

The ability of a (trained) observer to interpret the inner workings of a model. Neural networks are generally considered to be to complex to comprehend by humans and are treated as an ‘unexplainable’ black box. The lack of explainability is a major issue in many of the current clinical applications of neural networks.

Fullyconnected or linear layer

Common building block of neural networks in which every node (or every datapoint) in the input is connected to every node in the output of the layer. Through the weights that are associated with each connection the layer is able perform linear transformations of the input data. Together with non-linear activation functions, fully connected layers make up the most basic forms of neural networks.

KL Divergence

The Kullback-Leiber Divergence is a measure of similarity between two distributions.

Loss function

The loss function of the network defines the training objective of the neural network. The loss, the output of the loss function, is progressively minimized through backpropagation, allowing the network to learn and be optimized for its training objective.

MNIST

A commonly used dataset consisting of image of handwritten digits. MNIST is often used for small scale experiments because of the simplistic nature of the data.

PCA

Principal component analysis. A technique commonly used for dimension reduction. The technique involves the calculation of

Posterior collapse

A phenomenon that can occur during the train of variational autoencoder through which the reconstruction accuracy of the network decreases dramatically if the KL-divergence reduces to much.

Vector

A vector is a single row or column of numbers.

Matrix

A set consisting of multiple rows and columns of numbers.

Convergence

A neural network has reached convergence when further training does no longer improve the model.

MSE loss

Mean Squared Error loss, a measure of difference between two data instances such as images or timeseries. The MSE loss is a common loss function that is used to minimize the reconstruction error in auto-encoders.

Latent variable

A variable that is not directly observed in the data but can be inferred through the usage of a model from other variables that are observed directly. In the case of auto-encoders we refer to the variables in the vector extracted after applying the encoder of the auto-encoder as latent variables.

Disentanglement

The disentanglement of latent variables refers to the process of separating the influence of each latent variable on the reconstructed data.

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Vessies, M., van de Leur, R., Wouters, P., van Es, R. (2023). Deep Learning—Autoencoders. In: Asselbergs, F.W., Denaxas, S., Oberski, D.L., Moore, J.H. (eds) Clinical Applications of Artificial Intelligence in Real-World Data. Springer, Cham. https://doi.org/10.1007/978-3-031-36678-9_13

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  • DOI: https://doi.org/10.1007/978-3-031-36678-9_13

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