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Abstract #1804

Automatic Classification of MR Image Contrast

Julia Cluceru1, Yannet Interian2, Janine M. Lupo3, Riley Bove4, Atul Butte5, and Jason Crane3
1Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States, 2Program in Data Science, USF, San Francisco, CA, United States, 3Radiology and Biomedical Imagin, UCSF, San Francisco, CA, United States, 4Department of Neurology, UCSF, San Francisco, CA, United States, 5Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, United States

To perform large-scale analyses of disease progression, it is necessary to automate the retrieval and alignment of MR images of similar contrast. The goal of this study is to create an algorithm that can reliably classify brain exams by MR image contrast. We use two modeling strategies (SVM and CNN) and two training/testing cohorts to compare within-disease and between-disease transferability of the algorithms. For both cohorts, deep ResNets for extract imaging features combined in a random forest with DICOM metadata perform the best, resulting in 95.6% accuracy on the within-disease comparison, and 99.6% overall accuracy on between-disease comparison.

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