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Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection
[article]
2018
arXiv
pre-print
Unsupervised learning can leverage large-scale data sources without the need for annotations. In this context, deep learning-based auto encoders have shown great potential in detecting anomalies in medical images. However, state-of-the-art anomaly scores are still based on the reconstruction error, which lacks in two essential parts: it ignores the model-internal representation employed for reconstruction, and it lacks formal assertions and comparability between samples. We address these
arXiv:1812.05941v1
fatcat:xnellrlzo5g6tdnj3nhr5jzgva