Medical Image Segmentation with Minimal Labeling Effort: How Far Can We Push the Limits?

Abstract

We demonstrate for the first time that a medical image segmentation model can achieve near fully supervised performance using only a single annotated image and abundant unlabeled data. We present MedSMILE, a novel framework that synergistically integrates transductive and inductive learning for this extreme one-label semi-supervised setting. Its core novelty lies in an iterative loop where a foundation model both bootstraps and refines pseudo-labels for an inductive segmentation model. This process begins with the foundation model performing transductive inference to generate an initial set of pseudo-labels for the unlabeled data pool. This bootstraps an iterative self-training process where the segmentation model is trained and used to generate progressively better labels, with an inter-round refinement step that re-leverages the foundation model to correct errors in uncertain predictions. Experiments on seven datasets across four modalities show MedSMILE recovers 90%–95% of the fully supervised Dice score while decisively outperforming existing semi-supervised techniques that require substantially more annotation. MedSMILE sets a new standard for label-efficient learning in medical image segmentation.