Optimising Knee Injury Detection with Spatial Attention and Validating Localisation Ability
This work improves diagnostic accuracy for knee injuries in medical imaging, though it is incremental as it builds on existing CNN and attention methods.
The paper tackled knee injury detection from MRI scans by developing MPFuseNet, a multi-view CNN with spatial attention, achieving state-of-the-art AUC scores of 0.977 for ACL tears and 0.957 for abnormal MRIs, and introduced a metric to validate localization ability.
This work employs a pre-trained, multi-view Convolutional Neural Network (CNN) with a spatial attention block to optimise knee injury detection. An open-source Magnetic Resonance Imaging (MRI) data set with image-level labels was leveraged for this analysis. As MRI data is acquired from three planes, we compare our technique using data from a single-plane and multiple planes (multi-plane). For multi-plane, we investigate various methods of fusing the planes in the network. This analysis resulted in the novel 'MPFuseNet' network and state-of-the-art Area Under the Curve (AUC) scores for detecting Anterior Cruciate Ligament (ACL) tears and Abnormal MRIs, achieving AUC scores of 0.977 and 0.957 respectively. We then developed an objective metric, Penalised Localisation Accuracy (PLA), to validate the model's localisation ability. This metric compares binary masks generated from Grad-Cam output and the radiologist's annotations on a sample of MRIs. We also extracted explainability features in a model-agnostic approach that were then verified as clinically relevant by the radiologist.