Confidence-Aware Supervision for Robust Multi-task Embryo Grading

Abstract

Multi-task learning is widely adopted for automated embryo grading. However, the reliability of predicted confidence across heterogeneous grading components remains underexplored. We observed that blastocyst development stage, inner cell mass, and trophectoderm exhibit distinct uncertainty characteristics, and that overconfident predictions may become more evident when outputs are jointly evaluated. To improve robustness under heterogeneous uncertainty, we propose a confidence-aware soft-label learning framework that selectively refines supervision based on feature consistency and entropy stability. Unlike uniform regularization, the proposed approach preserves discriminative structure while reducing excessive confidence in unstable spaces. Experiments on 28,670 Day-5 embryo images from seven IVF clinics showed that our method achieves the highest overall mF1 (0.7218) while reducing overconfident error rates relative to baseline training. When combined with temperature scaling, calibration further improves without performance degradation. These findings highlight that uncertainty-aware supervision provides a practical strategy for enhancing reliability in multi-task medical AI systems.