\section{Related Work}

\paragraph{Deep Learning in the Knee.}
Deep learning methodologies have been exploited for different medical tasks on the knee.
Classification-based and segmentation-based CNNs were used to assess lesions of the knee cartilage on MRI images \citep{Liu2018Radiology}. On the same knee structure but imaged with ultrasound, U-Net \citep{Ronneberger2015} based solutions have been used for knee femoral cartilage segmentation \citep{Antico2020} and tracking \citep{Dunnhofer2020MedIA} during minimally invasive surgeries.
3D CNNs were proposed to increase the resolution of knee MRI images \citep{Chaudhari2018}, while deep siamese CNNs were shown to score effectively the severity of knee osteoarthritis \citep{Tiulpin2018}.
\citet{Liu2019} employed a cascade of CNNs to diagnose ACL tears, while \citet{MRNet}, \citet{ELNet}, and \citet{Azcona2020} showed that single CNNs for image classification are general enough to detect disparate abnormalities of the knee including ACL and meniscal tears.
Our proposed \myalgoname\ solution addresses the same problem tackled by the latter methods, but introduces a new CNN architecture that takes into account the anatomy of knee disorders to better capture its appearance features.

\paragraph{Feature Pyramids.}
Feature pyramids have been extensively used in computer vision to tackle different problems related to object detection.
In the setting of natural images, feature pyramid networks were exploited to learn more efficient image representations \citep{FPN,PanoFPN}.
Towards more application-specific solutions, feature pyramids have been used for shadow detection \citep{Zhu2018}, pavement crack detection \citep{Yang2020}, land segmentation \citep{Seferbekov2018}, person re-identification \citep{Martinel2019cvpr}, person pose estimation \citep{Chen2018cvpr}, and multi-object tracking \citep{Lee2019}.
In the context of medical image analysis, feature pyramids have been studied for the detection of pulmonary pathologies \citep{Xiao2019icassp} and pulmonary nodules \citep{Liu2019miccai}, or for the diagnosis of cervical cancer \citep{Xu2015} and lung carcinoma \citep{Hao2020}. 
Currently, no method is present to tackle the problem of knee pathology detection through the adoption of feature pyramids.