Knee Injury Diagnosis with Data and Feature Fusion-Enhanced Multi-Label Classification Network

Knee injuries are among the most common human afflictions despite age, gender, or lifestyle. They are often detected using a non-invasive diagnostic tool such as Magnetic Resonance Imaging (MRI) of the knee. With the rising number of injuries yearly, the risk of diagnostic errors by radiologists increase exponentially. In order to mitigate this, deep learning methods have been used for designing an automatic MRI interpreter. MRNet public dataset is an established knee injury dataset consisting of multi-view MRI images with multi-label classification. The prior deep learning solutions utilizing this dataset including the MRNet model, ignore the relationship and dependency among the different injuries and views. Given the specific properties of this dataset and problem, we propose a tailored multi-label classification network with enhanced data and feature fusion. The designed model out-performs the MRNet baseline model in terms of complexity and performance reaching an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.925.