Improving MRI-based Knee Disorder Diagnosis with Pyramidal Feature DetailsDownload PDF

Published: 31 Mar 2021, Last Modified: 16 May 2023MIDL 2021Readers: Everyone
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
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Paper Type: both
Primary Subject Area: Detection and Diagnosis
Secondary Subject Area: Unsupervised Learning and Representation Learning
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