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Anomaly Detection Through Latent Space Restoration Using Vector Quantized Variational Autoencoders.

, and . ISBI, page 1764-1767. IEEE, (2021)

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Implicit Field Learning for Unsupervised Anomaly Detection in Medical Images., and . MICCAI (2), volume 12902 of Lecture Notes in Computer Science, page 189-198. Springer, (2021)Abstract: MOOD 2020 - A Public Benchmark for Out-of-distribution Detection and Localization on Medical Images., , , , , , , , , and 24 other author(s). Bildverarbeitung für die Medizin, page 312. Springer, (2023)Anomaly detection through latent space restoration using vector-quantized variational autoencoders., and . CoRR, (2020)DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection., , , , , and . CoRR, (2023)Implicit U-Net for Volumetric Medical Image Segmentation., and . MIUA, volume 13413 of Lecture Notes in Computer Science, page 387-397. Springer, (2022)ARIA: On the interaction between Architectures, Aggregation methods and Initializations in federated visual classification., , , and . CoRR, (2023)Anomaly Detection Through Latent Space Restoration Using Vector Quantized Variational Autoencoders., and . ISBI, page 1764-1767. IEEE, (2021)MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution Detection in Medical Images., , and . DGM4MICCAI, volume 14533 of Lecture Notes in Computer Science, page 35-44. Springer, (2023)MOOD 2020: A Public Benchmark for Out-of-Distribution Detection and Localization on Medical Images., , , , , , , , , and 24 other author(s). IEEE Trans. Medical Imaging, 41 (10): 2728-2738 (2022)