Improving MRI-based Knee Disorder Diagnosis with Pyramidal Feature Details

Matteo Dunnhofer, Niki Martinel, Christian Micheloni

Keywords: Knee Disorder Diagnosis, MRI, Pyramidal Detail Pooling, Feature Pyramid Network, ACL Tear Detection, Meniscus Tear Detection

TL;DR: We propose MRPyrNet, a CNN strategy to improve the capabilities of state-of-the-art solutions in the ACL and meniscal tear detection.

Abstract: This paper presents MRPyrNet, a new convolutional neural network (CNN) architecture that improves the capabilities of CNN-based pipelines for knee injury detection via magnetic resonance imaging (MRI). Existing works showed that anomalies are localized in small-sized knee regions that appear in particular areas of MRI scans. Based on such facts, MRPyrNet exploits a Feature Pyramid Network to enhance small appearing features and Pyramidal Detail Pooling to capture such relevant information in a robust way. Experimental results on two publicly available datasets demonstrate that MRPyrNet improves the ACL tear and meniscal tear diagnosis capabilities of two state-of-the-art methodologies. Code is available at https://git.io/JtMPH.

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Paper Type: both

Primary Subject Area: Detection and Diagnosis

Secondary Subject Area: Unsupervised Learning and Representation Learning

Source Code Url: https://git.io/JtMPH

Data Set Url:

https://stanfordmlgroup.github.io/competitions/mrnet/
http://www.riteh.uniri.hr/~istajduh/projects/kneeMRI/