Abstract
Data augmentation is widely utilized to achieve more generalizable and accurate deep learning models based on relatively small labeled datasets. Data augmentation techniques are particularly critical in medical applications, where access to labeled data samples is commonly limited. Although data augmentation methods generally have a positive impact on the performance of deep learning models, not all data augmentation techniques are applicable and suitable for analyzing medical images. In this chapter, we review common image augmentation techniques and their properties. Furthermore, we present and evaluate application-specific data augmentation methods that are beneficial for medical image analysis. The material presented in this chapter aims to guide the use of data augmentation techniques in training deep learning models for various medical image analysis applications, in which annotated data are not abundant or are difficult to acquire.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
He, K., et al.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision (2015)
Bengio, Y.: Learning deep architectures for AI. Found. Trends® Mach. Learn. 2(1), 1–127 (2009)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533 (1986)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int J Comput Vision 115(3), 211–252 (2015)
Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: Icdar. IEEE (2003)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Eaton-Rosen, Z., et al.: Improving data augmentation for medical image segmentation (2018)
Izadi, S., et al.: Generative adversarial networks to segment skin lesions. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE (2018)
Frid-Adar, M., et al.: Synthetic data augmentation using GAN for improved liver lesion classification. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE (2018)
Larson, D.B., et al.: Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology 287(1), 313–322 (2017)
Lee, H., et al.: Fully automated deep learning system for bone age assessment. J Digit Imaging 30(4), 427–441 (2017)
Fischer, A.H., et al.: Hematoxylin and eosin staining of tissue and cell sections. Cold Spring Harbor Protoc. 2008(5), pdb. prot4986 (2008)
Biberacher, V., et al.: Intra-and interscanner variability of magnetic resonance imaging based volumetry in multiple sclerosis. Neuroimage 142, 188–197 (2016)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer (2015)
Castro, E., Cardoso, J.S., Pereira, J.C.: Elastic deformations for data augmentation in breast cancer mass detection. In: 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE (2018)
Christ, P.F., et al.: Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks. arXiv:1702.05970 (2017)
Sugino, T., et al.: Automatic segmentation of eyeball structures from micro-CT images based on sparse annotation. In: Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging. International Society for Optics and Photonics (2018)
Zhang, H., et al.: Mixup: Beyond empirical risk minimization. arXiv:1710.09412 (2017)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (2014)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434 (2015)
Salimans, T., et al.: Improved techniques for training gans. In: Advances in Neural Information Processing Systems (2016)
Paszke, A., et al.: PyTorch (2017)
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: OSDI (2016)
https://www.tensorflow.org/api_docs/python/tf/contrib/image.
arXiv:1606.00897Bauer, S., et al.: Multi-organ cancer classification and survival analysis. (2016)
Korbar, B., et al.: Deep learning for classification of colorectal polyps on whole-slide images. J. Pathol. Inf. 8 (2017)
Veta, M., Van Diest, P.J., Pluim, J.P.: Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer (2016)
Maninis, K.-K., et al.: Deep retinal image understanding. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer (2016)
Yang, Y., et al.: Lesion detection and grading of diabetic retinopathy via two-stages deep convolutional neural networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer (2017)
Galdran, A., et al.: Data-driven color augmentation techniques for deep skin image analysis. arXiv:1703.03702 (2017)
Tomita, N., et al.: Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans.98, 8–15 (2018)
Pereira, S., et al.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 35(5), 1240–1251 (2016)
Madani, A., et al.: Chest x-ray generation and data augmentation for cardiovascular abnormality classification. In: Medical Imaging 2018: Image Processing. International Society for Optics and Photonics (2018)
Quan, T.M., Hildebrand, D.G., Jeong, W.-K.: Fusionnet: a deep fully residual convolutional neural network for image segmentation in connectomics (2016)
Goodfellow, I., et al.: Deep learning, vol. 1. MIT press, Cambridge (2016).
Abrà moff, M.D., et al.: Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 57(13), 5200–5206 (2016)
BenTaieb, A., Hamarneh, G.: Topology aware fully convolutional networks for histology gland segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Abdollahi, B., Tomita, N., Hassanpour, S. (2020). Data Augmentation in Training Deep Learning Models for Medical Image Analysis. In: Nanni, L., Brahnam, S., Brattin, R., Ghidoni, S., Jain, L. (eds) Deep Learners and Deep Learner Descriptors for Medical Applications. Intelligent Systems Reference Library, vol 186. Springer, Cham. https://doi.org/10.1007/978-3-030-42750-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-42750-4_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-42748-1
Online ISBN: 978-3-030-42750-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)