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English(EN) Lost in the Folds: When Cross-Validation Is Not a Deep Ensemble for Uncertainty Estimation

研究论文区分了用于人工智能不确定性的交叉验证与深度集成

作者 PulseAugur 编辑部 · [3 个来源] ·

一篇题为“折叠中的迷失”的新研究论文强调了人工智能研究中关于医学图像分割不确定性估计的一个普遍误解。研究表明，使用K折交叉验证（CV）来形成集成模型，通常被错误地标记为深度集成（DE），这可能导致对不确定性的不准确解读。研究发现，使用相同训练数据但不同随机种子的DE更适合故障检测等可靠性任务，而CV集成模型更适合建模模糊性。 AI

影响 阐明了人工智能中不确定性估计的最佳实践，影响了医学影像中的可靠性和模糊性建模。

排序理由 该集群包含一篇讨论新方法论及其对人工智能研究影响的学术论文。

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研究论文区分了用于人工智能不确定性的交叉验证与深度集成

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报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Tristan Kirscher (ICube, Institut Strauss, DKFZ), Markus Bujotzek (DKFZ), Yannick Kirchhoff (DKFZ), Maximilian Rokuss (DKFZ), Fabian Isensee (DKFZ), Kim-Celine Kahl (DKFZ), Balint Kovacs (DKFZ), Klaus Maier-Hein (DKFZ) ·

    折叠中的迷失：交叉验证何时并非不确定性估计的深度集成

    arXiv:2605.18329v2 Announce Type: replace-cross Abstract: Ensemble disagreement is widely used as a proxy for epistemic uncertainty in medical image segmentation. In practice, many studies form ensembles via K-fold cross-validation (CV), yet refer to them as ``deep ensembles'' (D…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    折叠中的迷失：交叉验证何时不是用于不确定性估计的深度集成

    Ensemble disagreement is widely used as a proxy for epistemic uncertainty in medical image segmentation. In practice, many studies form ensembles via K-fold cross-validation (CV), yet refer to them as ``deep ensembles'' (DE). Because CV members are trained on different data subse…

  3. arXiv cs.CV TIER_1 English(EN) · Maier-Hein Klaus ·

    折叠中的迷失：交叉验证何时不是用于不确定性估计的深度集成

    Ensemble disagreement is widely used as a proxy for epistemic uncertainty in medical image segmentation. In practice, many studies form ensembles via K-fold cross-validation (CV), yet refer to them as ``deep ensembles'' (DE). Because CV members are trained on different data subse…

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