Anatomical labeling of brain CT scan anomalies using multi-context nearest neighbor relation networks
This work addresses the need for accurate anatomical labeling in medical imaging, which is crucial for diagnosis and treatment planning, but it appears incremental as it builds upon existing Relation Networks with a specific training improvement.
The authors tackled the problem of automated anatomical labeling in brain CT scans by developing a deep learning method that combines local and global context with Relation Networks and a novel nearest neighbors training strategy, achieving increased performance compared to baseline methods.
This work is an endeavor to develop a deep learning methodology for automated anatomical labeling of a given region of interest (ROI) in brain computed tomography (CT) scans. We combine both local and global context to obtain a representation of the ROI. We then use Relation Networks (RNs) to predict the corresponding anatomy of the ROI based on its relationship score for each class. Further, we propose a novel strategy employing nearest neighbors approach for training RNs. We train RNs to learn the relationship of the target ROI with the joint representation of its nearest neighbors in each class instead of all data-points in each class. The proposed strategy leads to better training of RNs along with increased performance as compared to training baseline RN network.