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In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation (SAS) in Magnetic Resonance Images (MRI) of the brain, which is the task of automatically identifying pathologies in brain images.
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Pawlowski, N., Lee, M.C., Rajchl, M., McDonagh, S., Ferrante, E., Kamnitsas, K., Cooke, S., Stevenson, S., Khetani, A., Newman, T., et al.: Unsupervised lesion detection in brain ct using bayesian convolutional autoencoders. In: MIDL 2018 Conference book. MIDL (April 2018)
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Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R.T., Berger, C., Ha, S.M., Rozycki, M., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge (2019)
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Baur, C., Wiestler, B., Albarqouni, S., Navab, N.: Deep autoencoding models for unsupervised anomaly segmentation in brain mr images. Lecture Notes in Computer Science p. 161–169 (2019). https://doi.org/10.1007/978-3-030-11723-8_16
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2020
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Baur, C., Wiestler, B., Albarqouni, S., Navab, N.: Bayesian skip-autoencoders for unsupervised hyperintense anomaly detection in high resolution brain mri. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). pp. 1905–1909 (2020). https://doi.org/10.1109/ISBI45749.2020.9098686
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Baur, C., Wiestler, B., Albarqouni, S., Navab, N.: Scale-space autoencoders for unsupervised anomaly segmentation in brain mri. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 552–561. Springer, Cham (2020)
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Chen, X., You, S., Tezcan, K.C., Konukoglu, E.: Unsupervised lesion detection via image restoration with a normative prior. Medical Image Analysis 64
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2019
Cited alongside, same era.
Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving unsupervised defect segmentation by applying structural similarity to autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019). https://doi.org/10.5220/0007364503720380
2019
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Kuijf, H.J., Casamitjana, A., Collins, D.L., Dadar, M., Georgiou, A., Ghafoorian, M., Jin, D., Khademi, A., Knight, J., Li, H., et al.: Standardized assessment of automatic segmentation of white matter hyperintensities and results of the wmh segmentation challenge. IEEE Trans. Med. Imaging 38
2019
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Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54
2019
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Zimmerer, D., Isensee, F., Petersen, J., Kohl, S., Maier-Hein, K.: Unsupervised anomaly localization using variational auto-encoders. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. pp. 289–297. Springer International Publishing (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Later among the works it cites.
Dehaene, D., Frigo, O., Combrexelle, S., Eline, P.: Iterative energy-based projection on a normal data manifold for anomaly localization. In: International Conference on Learning Representations (2020)
2020
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Liu, W., Li, R., Zheng, M., Karanam, S., Wu, Z., Bhanu, B., Radke, R.J., Camps, O.: Towards visually explaining variational autoencoders. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 8639–8648 (2020). https://doi.org/10.1109/CVPR42600.2020.00867
2020
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Yi, J., Yoon, S.: Patch svdd: Patch-level svdd for anomaly detection and segmentation. In: Proceedings of the Asian Conference on Computer Vision (ACCV) (November 2020)
2020
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Pinaya, W.H.L., Tudosiu, P.D., Gray, R., Rees, G., Nachev, P., Ourselin, S., Cardoso, M.J.: Unsupervised brain anomaly detection and segmentation with transformers (2021)
2021
Closest in time.
vanHespen, K.M., Zwanenburg, J.J.M., Dankbaar, J.W., Geerlings, M.I., Hendrikse, J., Kuijf, H.J.: An anomaly detection approach to identify chronic brain infarcts on mri. Scientific Reports 11
2021
Closest in time.