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Abstract #1304

Attention-based convolutional network quantifying the importance of quantitative MR metrics in the multiple sclerosis lesion classification  

Po-Jui Lu1,2,3, Reza Rahmanzadeh1,2, Riccardo Galbusera1,2, Matthias Weigel1,2,4, Youngjin Yoo3, Pascal Ceccaldi3, Yi Wang5, Jens Kuhle2, Ludwig Kappos1,2, Philippe Cattin6, Benjamin Odry7, Eli Gibson3, and Cristina Granziera1,2
1Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, 2Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, 3Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, United States, 4Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland, 5Department of Radiology, Weill Cornell Medical College, New York, NY, United States, 6Center for medical Image Analysis & Navigation, Department of Biomedical Engineering, University of Basel, Basel, Switzerland, 7Covera Health, New York, NY, United States

White matter lesions in multiple sclerosis patients exhibit distinct characteristics depending on their locations in the brain. Multiple quantitative MR sequences sensitive to white matter micro-environment are necessary for the assessment of those lesions; but how to judge which sequences contain the most relevant information remains a challenge. In this abstract, we are proposing a convolutional neural network with a gated attention mechanism to quantify the importance of MR metrics in classifying juxtacortical and periventricular lesions. The results show the statistically significant order of quantitative importance of metrics, one step closer to combining more relevant metrics for better interpretation.

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