We present a systematic empirical study examining medical imaging across 30 benchmarks and 28,854 evaluation instances. Our analysis reveals that data augmentation plays a more critical role than previously recognized, achieving 0.
The Dice coefficient is the dominant evaluation metric in medical image segmentation, but its popularity may conceal an important limitation: in sparse-target settings, especially those involving small lesions, overlap-based summaries can understate clinically meaningful differences in boundary quality. We study this problem across 3 public lesion segmentation benchmarks spanning MRI, CT, and fundus imaging, comprising 5,842 annotated lesions and 4 representative model families evaluated under a standardized training and inference protocol.
Semantic segmentation quality measured by IoU treats all pixels equally, but boundary pixels are inherently ambiguous and annotator agreement drops to near-chance there. We propose Attention Map Entropy (AME) computed from self-attention maps at the penultimate layer of ViT-based segmentation models.