\section{Introduction}
Due to its complex and delicate anatomy, the knee is one of the body regions most susceptible to severe injuries. To evaluate and diagnose such disorders, magnetic resonance imaging (MRI) is currently the standard clinical modality.
This is because of the high accuracy this sensory system gives for the diagnosis of pathologies such as the anterior cruciate ligament (ACL) tear and meniscus tear \citep{Rangger1996,Mackenzie1996,Cheung1997,Oei2003}.
Indeed, the efficacy of MRI makes the knee the body structure where musculoskeletal (MSK) examinations are performed more. However, an accurate interpretation of knee MRIs requires a significant effort because of the volume and detail of information contained in MRI exams. As shown by \citet{Kim2008}, such a manual process can lead to improper outcomes 
even when performed by board-certified MSK radiologists.
To mitigate these problems, \citet{MRNet} recently proposed MRNet, a fully automated system for knee MRI interpretation based on convolutional neural networks (CNNs).
The solution presents a pipeline in which an AlexNet-based CNN architecture \citep{AlexNet} is trained to predict the presence or absence of ACL tear, meniscal tear, or generic abnormalities, given axial, coronal, and sagittal views of an MRI exam.
For the same tasks, \citet{ELNet} later proposed ELNet as an improved CNN architecture based on residual connections, normalization layers, and blur pooling.
\citet{Azcona2020} instead studied the impact of transfer learning and data augmentation techniques on deep residual models.

Despite the promising results, the aforementioned deep learning solutions neglected the particular anatomy of knee disorders. 
Indeed, the currently available methods learn an MRI image representation that ultimately compresses, by a global pooling operation, the 2D spatial information of CNN feature maps into a vector of single scalar values. We argue that such a process harms the detection of the relevant features of anomalies and that it ultimately leads to wrong diagnosis predictions in more difficult and ambiguous cases.
Several works \citep{Hash2013,Nguyen2014,kneeMRI,Lecouvet2018,MRNet} have shown that particular features associated with the diagnoses are localized in specific regions of the knee, that appear very small and \rev{often} localized \rev{around} the center of the MRI images. This is especially true for the ACL tear \citep{Hash2013} and meniscal tear \citep{Nguyen2014,Lecouvet2018}. 

Following such an intuition, we present \myalgoname, a novel CNN architecture capable of capturing the relevant information of knee disorders in such image areas.
Since feature pyramids \citep{Adelson1984} showed to be effective for the better detection of pathologies \citep{Liu2019miccai,Xiao2019icassp,Hao2020}, our solution comprises a Feature Pyramid Network (FPN) \citep{FPN} to better detect small-sized features, and a Pyramidal Detail Pooling (\modulename) module to analyze features at multiple levels of detail.
Our plug-and-play strategy can be applied to any existing backbone CNN. We show how to apply it to the pipelines of MRNet \citep{MRNet} and ELNet \citep{ELNet} \rev{for the detection of the ACL and meniscal tears.}
\rev{Our experiments indicate that including priors on the disorders is important to improve the diagnostic capabilities of state-of-the-art methodologies.}
