English(EN) SCALE: Synthetic Calibration via Agreement Labeling in Embedding Space

新方法无需多注释者标签即可改进病理模型校准

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员开发了一种名为合成协议校准的新方法，用于改进计算病理学中使用的基础模型的校准。该技术解决了模型可能对其疑难病例的预测过于自信的问题，即使平均准确率尚可。通过使用训练好的线性探针并在类别锚点之间进行插值，该方法创建了一种诊断模糊性的合成度量。这使得能够使用协议感知的标签平滑来重新训练探针，从而在无需多注释者标签的情况下增强校准，并保持判别指标。 AI

影响通过改进置信度校准，增强了人工智能模型在关键诊断任务中的可靠性。

排序理由该集群包含一篇详细介绍改进模型校准新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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arXiv cs.CV TIER_1 English(EN) · Wenjun Liu, Saeed Hassanpour · 2026-10-01 04:00

SCALE：嵌入空间中的一致性标签合成校准

arXiv:2609.38705v1 Announce Type: new Abstract: Foundation models for computational pathology are usually evaluated using AUC and accuracy, while calibration is often left untested. This matters because a model can be accurate on average but still assign overly confident probabil…

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SCALE：嵌入空间中的一致性标签合成校准

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