| [163164] |
| Title: Three-dimensional deep learning with spatial erasing for unsupervised anomaly segmentation in brain MRI. |
| Written by: M. Bengs and F. Behrendt and J. Krüger and R. Opfer and A. Schlaefer |
| in: <em>International Journal of Computer Assisted Radiology and Surgery</em>. (2021). |
| Volume: <strong>16</strong>. Number: (9), |
| on pages: 1413-1423 |
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| DOI: 10.1007/s11548-021-02451-9 |
| URL: https://doi.org/10.1007/s11548-021-02451-9 |
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Abstract: Brain Magnetic Resonance Images (MRIs) are essential for the diagnosis of neurological diseases. Recently, deep learning methods for unsupervised anomaly detection (UAD) have been proposed for the analysis of brain MRI. These methods rely on healthy brain MRIs and eliminate the requirement of pixel-wise annotated data compared to supervised deep learning. While a wide range of methods for UAD have been proposed, these methods are mostly 2D and only learn from MRI slices, disregarding that brain lesions are inherently 3D and the spatial context of MRI volumes remains unexploited.