Adjacency-Aware Co-Occurrence Loss
Cross-source consensus on Adjacency-Aware Co-Occurrence Loss from 1 sources and 4 claims.
1 sources · 4 claims
How it works
Benefits
Highlighted claims
- The adjacency-aware co-occurrence loss uses a binary anatomical adjacency matrix to encode connected artery pairs. — AG-TAL: Anatomically-Guided Topology-Aware Loss for Multiclass Segmentation of the Circle of Willis Using Large-Scale Multi-Center Datasets
- The co-occurrence loss penalizes predicted co-occurrence for non-adjacent artery pairs. — AG-TAL: Anatomically-Guided Topology-Aware Loss for Multiclass Segmentation of the Circle of Willis Using Large-Scale Multi-Center Datasets
- For missing expected connections, the method compares predicted and ground-truth co-occurrence features in keypoint regions with Sobel-derived edge maps. — AG-TAL: Anatomically-Guided Topology-Aware Loss for Multiclass Segmentation of the Circle of Willis Using Large-Scale Multi-Center Datasets
- Adding co-occurrence loss improved overall consistency and reduced misclassification. — AG-TAL: Anatomically-Guided Topology-Aware Loss for Multiclass Segmentation of the Circle of Willis Using Large-Scale Multi-Center Datasets