Calibrating rater differences in prototype space with attention lets few-shot medical segmentation emit per-rater predictions and raise Dice on CURVAS and QUBIQ
The work formalizes few-shot multi-rater medical image segmentation and proposes a prototype-centric personalization framework: a consensus mask and consensus prototype are averaged from multi-rater masks, each rater prototype's deviation from the consensus prototype serves as the attention key, and a shared self-attention module calibrates the concatenated rater prototypes, combined with a calibration loss, pseudo-style supervision synthesized from superpixel pseudo labels via random structured boundary transformations, and two-stage training; on CURVAS abdominal CT (kidney, pancreas, liver; three experts; 20 training and 65 test scans) and QUBIQ brain-growth MRI (one class, seven raters; 34 training and 5 test scans), evaluated by per-rater Dice, the method improves consistently over pro