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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