English(EN) Are We Overconfident in Models and Results for Semi-Supervised 3D Medical Image Segmentation?

医学AI研究指出分割模型存在过度自信问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-26 04:00

研究人员发现，在3D医学图像分割的半监督学习中存在严重的过度自信问题。他们认为，当前的方法常常将预测置信度与真实不确定性混淆，导致确认偏差。此外，许多基准测试中缺乏专门的验证集，鼓励使用测试集进行验证，从而夸大了性能估计，并造成了过拟合的“军备竞赛”。为解决此问题，提出了一种新框架，该框架明确区分了置信度和不确定性，并纠正了不同数据空间中的偏差，提倡更严格的基准测试实践。 AI

影响强调了医学影像AI能力可能被高估，呼吁进行更稳健的评估以确保可靠的临床应用。

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arXiv cs.CV TIER_1 English(EN) · Jun Li, Ziwei Qin · 2026-05-26 04:00

我们是否对半监督式3D医学图像分割的模型和结果过于自信？

arXiv:2605.25561v1 Announce Type: new Abstract: Semi-supervised learning has become a dominant paradigm for reducing annotation costs. However, we argue that the current progress is clouded by a twofold overconfidence problem. Algorithmically, mainstream pseudo-labeling framework…

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我们是否对半监督式3D医学图像分割的模型和结果过于自信？

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