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Abstract #3565

Retraining a Deep Learning Model to Detect Cerebral Microbleeds Using Single-Echo Stroke Data

Miller Fawaz1, Saifeng Liu1, David Utriainen1, Sean Sethi1, Zhen Wu1, and E. Mark Haacke1
1Magnetic Resonance Innovations, Inc., Bingham Farms, MI, United States

Automatic cerebral microbleed detection is attainable with our two step model for many disease states. We attributed previously shown lower performance in stroke data to different scenarios unique to stroke, including asymmetrically prominent cortical veins. We improved our existing pipeline for this detection by retraining the deep learning step of our model using stroke cases both in the acute and subacute stages. The results were improved performance in validation data in stroke cases as well as our previously tested data (multiple diseases). This makes our pipeline a viable and versatile real time automatic microbleed detection procedure.

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