mlsanityData to 7 Oct 2026

Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantification

arXiv 2411.07097 ↗First posted 11 Nov 2024cs.CVcited by 4

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Abstract (arXiv)

Uncertainty Quantification (UQ) is crucial for reliable image segmentation. Yet, while the field sees continual development of novel methods, a lack of agreed-upon benchmarks limits their systematic comparison and evaluation: Current UQ methods are typically tested either on overly simplistic toy datasets or on complex real-world datasets that do not allow to discern true uncertainty. To unify both controllability and complexity, we introduce Arctique, a procedurally generated dataset modeled after histopathological colon images. We chose histopathological images for two reasons: 1) their complexity in terms of intricate object structures and highly variable appearance, which yields challenging segmentation problems, and 2) their broad prevalence for medical diagnosis and respective relevance of high-quality UQ. To generate Arctique, we established a Blender-based framework for 3D scene creation with intrinsic noise manipulation. Arctique contains 50,000 rendered images with precise masks as well as noisy label simulations. We show that by independently controlling the uncertainty in both images and labels, we can effectively study the performance of several commonly used UQ methods. Hence, Arctique serves as a critical resource for benchmarking and advancing UQ techniques and other methodologies in complex, multi-object environments, bridging the gap between realism and controllability. All code is publicly available, allowing re-creation and controlled manipulations of our shipped images as well as creation and rendering of new scenes.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 27 of its 42 references that are in the map. First posted on arXiv 11 Nov 2024.

Builds on

27 of 42 references.

References · 27

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15 more
  1. 2019Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Visioncited by 182
  2. 2018Evaluating Bayesian Deep Learning Methods for Semantic Segmentationcited by 143
  3. 2021Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classificationcited by 89
  4. 2021On the Practicality of Deterministic Epistemic Uncertaintycited by 62
  5. 2022Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Taskscited by 49
  6. 2023FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methodscited by 49
  7. 2021Is segmentation uncertainty useful?cited by 37
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  11. 2017Active One-shot Learningcited by 21
  12. 2024Tyche: Stochastic In-Context Learning for Medical Image Segmentationcited by 20
  13. 2021Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentationcited by 19
  14. 2022Active learning using adaptable task-based prioritisationcited by 6
  15. 2022Panoptic segmentation with highly imbalanced semantic labelscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025PhenoBench: A Comprehensive Benchmark for Cell Phenotypingcited by 1
  2. 2026Better than Average: Spatially-Aware Aggregation of Segmentation Uncertainty Improves Downstream Performancecited by 1
  3. 2026Uncertainty-Aware Deep Learning for Genomics Applications: Insights from an Empirical Studycited by 0
  4. 2026A Multi-Annotator Study of Segmentation Noise and Uncertainty in Turbid Underwater Imagescited by 0