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eess.IV2020
Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI
Christoph Baur, Benedikt Wiestler, Shadi Albarqouni +1
Brain pathologies can vary greatly in size and shape, ranging from few pixels (i.e. MS lesions) to large, space-occupying tumors. Recently proposed Autoencoder-based methods for un…
eess.IV2020★ 1 cited
Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study
Christoph Baur, Stefan Denner, Benedikt Wiestler +2
Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these work…