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Research paper distinguishes cross-validation from deep ensembles for AI uncertainty

By PulseAugur Editorial · [3 sources] ·

A new research paper titled "Lost in the Folds" highlights a common misunderstanding in AI research regarding uncertainty estimation in medical image segmentation. The study reveals that using K-fold cross-validation (CV) to form ensembles, often mislabeled as deep ensembles (DE), can lead to inaccurate interpretations of uncertainty. DE, which use the same training data but different random seeds, are found to be better for reliability tasks like failure detection, while CV ensembles are more suited for modeling ambiguity. AI

IMPACT Clarifies best practices for uncertainty estimation in AI, impacting reliability and ambiguity modeling in medical imaging.

RANK_REASON The cluster contains an academic paper discussing a novel methodology and its implications for AI research.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Research paper distinguishes cross-validation from deep ensembles for AI uncertainty

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COVERAGE [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) ·

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

    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) ·

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

    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 ·

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

    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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