\section{Conclusions}
In this paper, we present \myalgoname, a CNN architecture to extract more relevant features of knee injuries imaged with MRI. Different works in the literature showed that particular disorders are localized in small-sized knee regions that appear \rev{around the center} of MRI scans. Through an FPN and a \modulename\ module, \myalgoname\ is capable of capturing the information in such areas more effectively. The proposed solution is general and, in this paper, it has been applied to the state-of-the-art diagnosis pipelines of MRNet and ELNet. Extensive experiments performed on two publicly available datasets demonstrate the effectiveness of the strategy in providing better diagnoses of ACL tear and meniscus tear. 
