CalcSeg combines confidence-aware curriculum learning with slice-wise self-attention to raise myocardial scar segmentation Dice to 0.677 and low-confidence Dice to 0.644 on single-stack LGE-CMR
The work presents CalcSeg, a confidence-aware latent 3D context curriculum learning framework that scores each sample using Dice, percentage scar-burden error, and epistemic uncertainty from Monte Carlo Dropout, expands training from easy to hard cases across stages, and uses slice-wise self-attention to infer subject-level 3D anatomical context from single-stack 2D LGE-CMR; on LGE-CMR data from four sites and two segmentation challenges (MICCAI 2012 LV Infarct, EMIDEC 2020) comprising 976 patients, it reaches myocardial scar Dice of 0.677±0.24, low-confidence Dice of 0.644±0.22, scar error of 38.88%, and low-confidence scar error of 35.03%, outperforming TransUNet, AttentionUNet, UNETR, ScarNet, and ScarNet with supervised curriculum learning using expert difficulty labels.