English(EN) Scalable Bayesian Gaussian Process Classification for Cancer Histopathology: Calibrated Uncertainty Under Distribution Shift

贝叶斯 GPC 框架增强癌症诊断不确定性量化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

研究人员开发了一个可扩展的贝叶斯高斯过程分类 (GPC) 框架，旨在提高癌症诊断模型的不确定性量化能力，特别是在跨不同医院应用时。这种新方法基于 Phikon 的冻结病理基础模型嵌入，结合了加速的随机枢轴乔列斯基分解和用于后验采样的汉密尔顿蒙特卡洛方法。该框架在 PCam 和 CAMELYON17-WILDS 等乳腺癌转移检测基准上进行了测试，显著降低了预期校准误差和假阴性率，证明了其在分布偏移下提供更可信的不确定性估计的有效性。 AI

影响提高医疗保健领域 AI 诊断工具的可信度，尤其是在不同临床地点之间。

排序理由该集群包含一篇详细介绍高斯过程分类新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yunru Zheng, Wun Ting Chan, Sampras Dsouza, Zhenyu Hong · 2026-11-06 05:00

可扩展的贝叶斯高斯过程分类用于癌症组织病理学：分布偏移下的校准不确定性

arXiv:2611.07127v1 Announce Type: new Abstract: Reliable uncertainty quantification is essential for clinical deployment of cancer diagnosis models, yet neural networks and variational Gaussian process methods can produce overconfident predictions, particularly under the distribu…

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报道来源 [1]

可扩展的贝叶斯高斯过程分类用于癌症组织病理学：分布偏移下的校准不确定性

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