Abstract
Data validation is the process of ensuring that the input to a data processing pipeline is correct and useful. It is a critical part of software systems running in production. Image processing systems are no different, whereby problems with data acquisition, file corruption or data transmission, may lead to a wide range of unexpected issues in the acquired images. Until now, most image processing systems of this type involved a human in the loop that could detect these errors before further processing. With the advent of powerful deep learning methods, tools for medical image processing are becoming increasingly autonomous and can go from data acquisition to final medical diagnosis without any human interaction. However, deep networks are known for their inability to detect corruption or errors in the input data. To overcome this, we present a validation method that learns the appearance of images in the training dataset that was used to train the deep network, and is able to identify when an input image deviates from the training distribution and therefore cannot be safely analyzed. We experimentally assess the validity of our method and compare it with different baselines, reaching an improvement of more than 10% points on all considered datasets.
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
Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)
Désir, C., Bernard, S., Petitjean, C., Heutte, L.: A random forest based approach for one class classification in medical imaging. In: Machine Learning in Medical Imaging, pp. 250–257 (2012)
Dinh, L., Sohl-Dickstein, J., Bengio, S.: Density estimation using Real NVP. In: International Conference on Learning Representations (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2014)
Kingma, D.P., Dhariwal, P.: Glow: generative flow with invertible 1x1 convolutions. In: Conference on Neural Information Processing Systems, pp. 10215–10224 (2018)
Lakovidis, D.K., Georgakopoulos, S.V., Vasilakakis, M., Koulaouzidis, A., Plagianakos, V.P.: Detecting and locating gastrointestinal anomalies using deep learning and iterative cluster unification. IEEE Trans. Med. Imaging 37(10), 2196–2210 (2018)
Lekadir, K., Merrifield, R., Yang, G.: Outlier detection and handling for robust 3-D active shape models search. IEEE Trans. Med. Imaging 26(2), 212–222 (2007)
Liu, Z., et al.: Quality control of diffusion weighted images. Soc. Photo Opt. Instrum. Eng. textbf7628 (2010). https://doi.org/10.1117/12.844748
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BraTS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)
Nalisnick, E., Matsukawa, A., Whye Teh, Y., Gorur, D., Lakshminarayanan, B.: Do deep generative models know what they don’t know? In: International Conference on Learning Representations (2019)
Ruff, L., et al.: Deep one-class classification. In: International Conference on Machine Learning, vol. 80, pp. 4393–4402 (2018)
Stanford University: Stanford Medicine 2018 Health Trends Report (2018)
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-Ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Computer Vision and Pattern Recognition (2017)
Woodard, J.P., Carley-Spencer, M.P.: No-reference image quality metrics for structural MRI. Neuroinformatics 4(3), 243–262 (2006). https://doi.org/10.1385/NI:4:3:243
Acknowledgments
This work received partial financial support from the Innosuisse Grant #6362.1 PFLS-LS.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Márquez-Neila, P., Sznitman, R. (2019). Image Data Validation for Medical Systems. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-32251-9_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32250-2
Online ISBN: 978-3-030-32251-9
eBook Packages: Computer ScienceComputer Science (R0)
