arXiv AI September 3, 2026 By Qin Lu, Zheyang Jing, Yujie Yang, Jianwang Li, Chen Yi, Shaofeng Jiang

SAUF-Net: Structure--Appearance Representation Learning with Uncertainty Feedback for Semi-Supervised Medical Image Segmentation

SAUF-Net is a semi‑supervised medical image segmentation framework that learns structure–appearance representations with uncertainty feedback. It decomposes bottleneck features into structural and appearance components, injects them into decoding, and uses auxiliary decoders and a dual‑head discriminator to estimate reliability and uncertainty. Experiments on ISIC‑2016 and Kvasir‑SEG show that SAUF‑Net surpasses state‑of‑the‑art methods, particularly when few labels are available.

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