Abstract
In medical imaging, un-, semi-, or self-supervised pathology detection is often approached with anomaly- or out-of-distribution detection methods, whose inductive biases are not intentionally directed towards detecting pathologies, and are therefore sub-optimal for this task. To tackle this problem, we propose AutoSeg, an engine that can generate diverse artificial anomalies that resemble the properties of real-world pathologies. Our method can accurately segment unseen artificial anomalies and outperforms existing methods for pathology detection on a challenging real-world dataset of Chest X-ray images. We experimentally evaluate our method on the Medical Out-of-Distribution Analysis Challenge 2021 (Code available under: https://github.com/FeliMe/autoseg).
G. Kaissis and D. Rueckert—Equal contribution.
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
Similar content being viewed by others
References
Atlason, H.E., Love, A., Sigurdsson, S., Gudnason, V., Ellingsen, L.M.: Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder. In: Medical Imaging 2019: Image Processing, March 2019. https://doi.org/10.1117/12.2512953
Baur, C., Denner, S., Wiestler, B., Navab, N., Albarqouni, S.: Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study. Med. Image Anal. 69, 101952 (2021)
Buda, M., Saha, A., Mazurowski, M.A.: Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm. Comput. Biol. Med. 109, 218–225 (2019). https://doi.org/10.1016/j.compbiomed.2019.05.002
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization (2019)
Meissen, F., Kaissis, G., Rueckert, D.: Challenging current semi-supervised anomaly segmentation methods for brain MRI (2021)
Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems vol. 32, pp. 8024–8035. Curran Associates, Inc. (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
Perslev, M., Dam, E.B., Pai, A., Igel, C.: One network to segment them all: a general, lightweight system for accurate 3D medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 30–38. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_4
Petersen, J., et al.: Medical out-of-distribution analysis challenge, March 2021. https://doi.org/10.5281/zenodo.4573948
Pinaya, W.H.L., et al.: Unsupervised brain anomaly detection and segmentation with transformers (2021)
Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30–44 (2019). https://doi.org/10.1016/j.media.2019.01.010
Smith, K., et al.: Data from CT colonography. The cancer imaging archive (2015). https://doi.org/10.7937/K9/TCIA.2015.NWTESAY1
Sun, D., et al.: Autoflow: learning a better training set for optical flow. In: CVPR (2021)
Tan, J., Hou, B., Batten, J., Qiu, H., Kainz, B.: Detecting outliers with foreign patch interpolation (2020)
Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., Ugurbil, K.: The WU-Minn human connectome project: an overview. NeuroImage 80, 62–79 (2013)
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-ray: hospital-scale chest X-ray database and benchmarks on weakly supervised classification and localization of common thorax diseases. In: Lu, L., Wang, X., Carneiro, G., Yang, L. (eds.) Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics. ACVPR, pp. 369–392. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-13969-8_18
Wood, E., et al.: Fake it till you make it: face analysis in the wild using synthetic data alone (2021)
Zimmerer, D., Isensee, F., Petersen, J., Kohl, S., Maier-Hein, K.: Unsupervised anomaly localization using variational auto-encoders. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 289–297. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_32
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Meissen, F., Kaissis, G., Rueckert, D. (2022). AutoSeg - Steering the Inductive Biases for Automatic Pathology Segmentation. In: Aubreville, M., Zimmerer, D., Heinrich, M. (eds) Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis. MICCAI 2021. Lecture Notes in Computer Science(), vol 13166. Springer, Cham. https://doi.org/10.1007/978-3-030-97281-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-97281-3_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-97280-6
Online ISBN: 978-3-030-97281-3
eBook Packages: Computer ScienceComputer Science (R0)