The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images
Open accessAbstract
Reliably modeling normality and differentiating abnormal appearances from normal cases is a very appealing approach for detecting pathologies in medical images. A plethora of such unsupervised anomaly detection approaches has been made in the medical domain, based on statistical methods, content-based retrieval, clustering and recently also deep learning. Previous approaches towards deep unsupervised anomaly detection model patches of normal anatomy with variants of Autoencoders or GANs, and detect anomalies either as outliers in the learned feature space or from large reconstruction errors. In contrast to these patch-based approaches, we show that deep spatial autoencoding models can be efficiently used to capture normal anatomical variability of entire 2D brain MR images. A variety of experiments on real MR data containing MS lesions corroborates our hypothesis that we can detect and even delineate anomalies in brain MR images by simply comparing input images to their reconstruction. Results show that constraints on the latent space and adversarial training can further improve the segmentation performance over standard deep representation learning.
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Cites
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Context Encoders: Feature Learning by Inpainting
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Sethian, J. A. (1999) · Medical Entomology and Zoology · Cited by 3,616
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., & Langs, G. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-59050-9_12 · Cited by 2,627
Learning Temporal Regularity in Video Sequences
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Autoencoding beyond pixels using a learned similarity metric
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Cited by
MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection
Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00982 · Cited by 2,203
Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings
Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00424 · Cited by 990
Gudovskiy, D., Ishizaka, S., & Kozuka, K. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00188 · Cited by 669
Bergmann, P., Batzner, K., Fauser, M., Sattlegger, D., & Steger, C. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01400-4 · Cited by 602
Anomaly detection of defects on concrete structures with the convolutional autoencoder
Chow, J. K., Su, Z., Wu, J., Tan, P. S., Mao, X., & Wang, Y. H. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101105 · Cited by 336
Metadata
- Authors
- Baur, C., Wiestler, B., Albarqouni, S., & Navab, N.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 161-169
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2796762894
- Cited by
- 432
- References
- 21
- FWCI
- 22.35
- Type
- conference paper
- Language
- en
- Open access
- green
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