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Abstract #3548

Visualizing intrinsic magnetic resonance imaging (MRI) dataset variations in image-space through Bayesian deep auto-encoding

Andrew P. Leynes1,2, Abhejit Rajagopal1, Valentina Pedoia1,2, and Peder E.Z. Larson1,2
1Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, 2UC Berkeley - UC San Francisco Joint Graduate Program in Bioengineering, Berkeley and San Francisco, CA, United States

We investigated the use of a Bayesian deep auto-encoder to visualize intrinsic variations within a dataset in image-space. The variations were visualized by calculating a voxel-wise standard deviation over the predictions of the Bayesian deep auto-encoder. The low mutual information that was measured between the MRI and the standard deviation maps suggests that new information is contained in the standard deviation maps. This may be useful in the training of deep learning models for anomaly detection.

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