J6 - Improving MRI-based Knee Disorder Diagnosis with Pyramidal Feature Details

Matteo Dunnhofer, Niki Martinel, Christian Micheloni

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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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Friday 9th July
J1-9 (short): Unsupervised and Representation Learning - 13:45 - 14:30 (UTC+2)
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