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Abstract #0983

Ex-vivo microbleed detection in community-based older adults using confidence aware learning

Grant Nikseresht1, Arnold M. Evia2, David A. Bennett2, Julie A. Schneider2, Gady Agam1, and Konstantinos Arfanakis2,3
1Computer Science, Illinois Institute of Technology, Chicago, IL, United States, 2Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States, 3Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Aging, Ex-Viov Applications, Neuro (Microbleeds)Detection of cerebral microbleeds (CMBs) on ex-vivo gradient echo MR images is an important task in MRI-pathology studies in older adults. The goal of this study was to develop a confidence-aware ex-vivo CMB detection algorithm that outputs interpretable probabilities and ranking of CMB candidates on brain MRI scans of community-based older adults. The present study demonstrates that training an ex-vivo CMB detection model with confidence-aware deep learning, a technique for improving confidence estimation and ordinal ranking of examples in classification models, improves detection performance and prediction interpretability.

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Keywords