I am happy to announce that our latest paper titled “Improving MRI-based Knee Disorder Diagnosis with Pyramidal Feature Details” has been presented last week at the Medical Imaging with Deep Learnign (MIDL) Conference 2021. In this work, we proposed a new convolutional neural network (CNN) architecture to improve the automatic diagnosis of knee disorders such as the ACL and the meniscus tear via magnetic resonance imaging.
Here is the 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.
and in the following you can access some resources (link to the paper, code, conference webpage for our paper, and video presentation).