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Cited by 188Artificial Intelligence in Healthcare and EducationMedical Image Segmentation TechniquesGenerative Adversarial Networks and Image Synthesis

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models

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Celard, P., Iglesias, E. L., Sorribes-Fdez, J. M., Romero, R., Vieira, A. Seara, Borrajo, L.·2023·Neural Computing and Applications
DOI 10.1007/s00521-022-07953-4PMC PMC9638354
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Abstract

Deep learning techniques, in particular generative models, have taken on great importance in medical image analysis. This paper surveys fundamental deep learning concepts related to medical image generation. It provides concise overviews of studies which use some of the latest state-of-the-art models from last years applied to medical images of different injured body areas or organs that have a disease associated with (e.g., brain tumor and COVID-19 lungs pneumonia). The motivation for this study is to offer a comprehensive overview of artificial neural networks (NNs) and deep generative models in medical imaging, so more groups and authors that are not familiar with deep learning take into consideration its use in medicine works. We review the use of generative models, such as generative adversarial networks and variational autoencoders, as techniques to achieve semantic segmentation, data augmentation, and better classification algorithms, among other purposes. In addition, a collection of widely used public medical datasets containing magnetic resonance (MR) images, computed tomography (CT) scans, and common pictures is presented. Finally, we feature a summary of the current state of generative models in medical image including key features, current challenges, and future research paths.

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  • Various challenges hinder the integration of AI models in clinical practice, including inconsistent data quality, absence of standardized performance metrics, and clinicians' diminished confidence in AI-generated diagnostic recommendations.

    “various challenges hinder the integration of AI models in clinical practice, including inconsistent data quality, the absence of standardized performance metrics, and clinicians’ diminished confidence in AI-generated diagnostic recommendations—such as lesion classification, malignancy risk evaluation, and triage recommendations (9–11).”

    From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [9]

    The Discussion’s “Challenges and future progress” section identifies shortages of disease-specific image datasets and the difficulty of expert labeling as obstacles to medical-image AI. It does not identify inconsistent data quality, a lack of standardized performance metrics, or diminished clinician confidence in diagnostic recommendations as those obstacles. The evaluation sections describe metrics but do not say that their standardization is lacking; page numbers were not available in our copy. (LLM, conf=0.78)

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  1. [9]Goodfellow I Pouget-Abadie J Mirza M Xu B Warde-Farley D Ozair S Courville A Bengio Y Generative adversarial nets Adv Neural Inf Process Syst 2014 27 2672 2680
  2. [11]Goodfellow I Bengio Y Courville A Deep learning 2016 Cambridge MIT Press
  3. [12]Clevert D-A, Unterthiner T, Hochreiter S (2016) Fast and accurate deep network learning by exponential linear units (elus). In: 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings. ICLR, San Juan, Puerto Rico
  4. [29]Sutskever I, Martens J, Dahl G, Hinton G (2013) On the importance of initialization and momentum in deep learning. In: Dasgupta S, McAllester D (eds) Proceedings of the 30th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol 28. PMLR, Atlanta, Georgia, USA, pp 1139–1147
  5. [32]Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: Bengio Y, LeCun Y (eds.), 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings
  6. [34]Reddi SJ, Kale S, Kumar S (2018) On the convergence of adam and beyond. In: 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings, Vancouver, Canada
  7. [52]Krizhevsky A Sutskever I Hinton GE Imagenet classification with deep convolutional neural networks Adv Neural Inf Process Syst 2012 25 1097 1105
  8. [54]Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. ICLR, San Diego, California
  9. [74]van den Oord A, Kalchbrenner N, Vinyals O, Espeholt L, Graves A, Kavukcuoglu K (2016) Conditional image generation with pixelcnn decoders. In: Advances in Neural Information Processing Systems, vol. 29. Curran Associates, Inc., Barcelona, Spain
  10. [76]Kingma D, Welling M (2014) Efficient gradient-based inference through transformations between bayes nets and neural nets. In: Proceedings of the 31st International Conference on Machine Learning, vol. 32, pp. 1782–1790. PMLR, Beijing, China
  11. [77]Rezende DJ, Mohamed S, Wierstra D (2014) Stochastic backpropagation and approximate inference in deep generative models. In: Proceedings of the 31st International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 32, pp. 1278–1286. PMLR, Bejing, China
  12. [90]Vaswani A Shazeer N Parmar N Uszkoreit J Jones L Gomez AN Kaiser LU Polosukhin I Guyon I Luxburg UV Bengio S Wallach H Fergus R Vishwanathan S Garnett R Attention is all you need Advances in neural information processing systems 2017 Long Beach, California Curran Associates Inc
  13. [95]Salimans T Goodfellow I Zaremba W Cheung V Radford A Chen X Chen X Lee D Sugiyama M Luxburg U Guyon I Garnett R Improved techniques for training gans Advances in neural information processing systems 2016 Barcelona, Spain Curran Associates Inc 2234 2242
  14. [96]Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. NIPS’17, pp. 6629–6640. Curran Associates Inc., Red Hook, NY, USA
  15. [100]Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: Precup D, Teh YW (eds.) Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 70, pp. 214–223. PMLR, Sydney, Australia
  16. [102]Radford A, Metz L, Chintala S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks. In: 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings, p. 149803. ICLR, San Juan, Puerto Rico
  17. [104]Karras T, Aila T, Laine S, Lehtinen J (2018) Progressive growing of gans for improved quality, stability, and variation. In: 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings, p. 149806. ICLR, Vancouver, Canada
  18. [108]Brock A, Donahue J, Simonyan K (2019) Large scale gan training for high fidelity natural image synthesis. In: 7th International Conference on Learning Representation 2019. ICLR, New Orleans
  19. [121]Davidson TR, Falorsi L, Cao ND, Kipf T, Tomczak JM (2018) Hyperspherical variational auto-encoders. In: 34th Conference on Uncertainty in Artificial Intelligence 2018, vol. 2, pp. 856–865. Association For Uncertainty in Artificial Intelligence, Monterey, California
  20. [124]Sohn K Lee H Yan X Learning structured output representation using deep conditional generative models Adv Neural Inf Process Syst 2015 28 3483 3491
  21. [129]Larsen ABL, Sønderby SK, Larochelle H, Winther O (2016) Autoencoding beyond pixels using a learned similarity metric. In: Balcan MF, Weinberger KQ (eds.), Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1558–1566. PMLR, New York, New York, USA
  22. [177]Song Y Ermon S Wallach H Larochelle H Beygelzimer A d’ Alché-Buc F Fox E Garnett R Generative modeling by estimating gradients of the data distribution Advances in neural information processing systems 2019 Vancouver, Canada Curran Associates Inc
  23. [178]Jalal A, Arvinte M, Daras G, Price E, Dimakis AG, Tamir J (2021) Robust compressed sensing mri with deep generative priors. In: Advances in Neural Information Processing Systems, vol. 34, pp. 14938–14954. Curran Associates, Inc., Virtual Conference

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Authors
Celard, P., Iglesias, E. L., Sorribes-Fdez, J. M., Romero, R., Vieira, A. S., & Borrajo, L.
Affiliations
Servicio Gallego de Salud (ES)
Corresponding author
Celard, P.
ORCID
Venue
Neural Computing and Applications
Publisher
Springer Science and Business Media LLC
Published
2022-11-04
Volume
35
Issue
3
Pages
2291-2323
ISSN
  • 0941-0643 (ISSN-L)
  • 1433-3058
DOI
10.1007/s00521-022-07953-4
PMC
PMC9638354
OpenAlex
W4308327397
Cited by
188
References
153
Citations by year
2026: 36 · 2025: 57 · 2024: 58 · 2023: 36 · 2022: 1
FWCI
2.60
Type
journal article
Language
en
Open access
Licence
Locations
  • Neural Computing and Applications (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Investigo Institutional repository of UVigo (Universidade de Vigo) (submittedVersion)
Funders
Xunta de Galicia (ED481A 2021/286)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
Keywords
deep learning for medical imaging, generative models, artificial neural networks, generative adversarial networks, variational autoencoder, medical image synthesis, semantic segmentation, data augmentation, medical image classification, brain tumors, COVID-19 pneumonia, magnetic resonance imaging, computed tomography, medical image dataset

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