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Abstract #1138

MRI-Based Multi-Task Deep Learning for Cartilage Lesion Severity Staging in Knee Osteoarthritis

Bruno Astuto1, Io Flament1, James Mitrani2, Rutwik Shah1,3, Matthew Bucknor1, Thomas Link1, Valentina Pedoia1,3, and Sharmila Majumdar1,3

1Department of Radiology and Biomedical Imaging, University of California San Francisco - UCSF, San Francisco, CA, United States, 2Lawrence Livermore National Lab, San Francisco, CA, United States, 3Center for Digital Health Innovation, UCSF, San Francisco, CA, United States

The automation of the grading task for the knee MRI scoring is appealing. The goal of this study is to leverage recent developments in Deep Learning (DL) applied to medical imaging in order to (i) identify cartilage lesions and assess severity (ii) identify the presence of BMELs, (ii)combine the two models in a multi-task automated and scalable fashion. We were able to boost performance of our final classifiers by not simply focusing on what the fine tuning of a single purpose model could offer, but rather broadly considering related tasks that could bring additional information to our classification problem.

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