mlsanityData to 7 Oct 2026

Are We Ready for Out-of-Distribution Detection in Digital Pathology?

arXiv 2407.13708 ↗First posted 18 Jul 2024cs.CV cs.LGcited by 8

Not on the map. Why?

Abstract (arXiv)

The detection of semantic and covariate out-of-distribution (OOD) examples is a critical yet overlooked challenge in digital pathology (DP). Recently, substantial insight and methods on OOD detection were presented by the ML community, but how do they fare in DP applications? To this end, we establish a benchmark study, our highlights being: 1) the adoption of proper evaluation protocols, 2) the comparison of diverse detectors in both a single and multi-model setting, and 3) the exploration into advanced ML settings like transfer learning (ImageNet vs. DP pre-training) and choice of architecture (CNNs vs. transformers). Through our comprehensive experiments, we contribute new insights and guidelines, paving the way for future research and discussion.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 29 of its 37 references that are in the map. First posted on arXiv 18 Jul 2024.

Builds on

29 of 37 references.

References · 29

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  4. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  5. 2022A ConvNet for the 2020scited by 3,545
  6. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,845
  7. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  8. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,282
  9. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,818
  10. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  11. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 596
  12. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
17 more
  1. 2019Scaling Out-of-Distribution Detection for Real-World Settingscited by 465
  2. 2022ViM: Out-Of-Distribution with Virtual-logit Matchingcited by 404
  3. 2019Using Pre-Training Can Improve Model Robustness and Uncertaintycited by 373
  4. 2021On the Importance of Gradients for Detecting Distributional Shifts in the Wildcited by 338
  5. 2023Quilt-1M: One Million Image-Text Pairs for Histopathologycited by 192
  6. 2021Are Transformers More Robust Than CNNs?cited by 187
  7. 2023OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detectioncited by 178
  8. 2019Ensemble Distribution Distillationcited by 158
  9. 2022Benchmarking Self-Supervised Learning on Diverse Pathology Datasetscited by 148
  10. 2022Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?cited by 124
  11. 2022A Call to Reflect on Evaluation Practices for Failure Detection in Image Classificationcited by 51
  12. 2020A Benchmark of Medical Out of Distribution Detectioncited by 40
  13. 2022An Impartial Take to the CNN vs Transformer Robustness Contestcited by 34
  14. 2020Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD Detectioncited by 26
  15. 2022Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathologycited by 26
  16. 2022Out-Of-Distribution Detection Is Not All You Needcited by 23
  17. 2021Are Bayesian neural networks intrinsically good at out-of-distribution detection?cited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 3.

8 papers.

  1. 2024Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advancescited by 24
  2. 2025Hierarchical Vision-Language Learning for Medical Out-of-Distribution Detectioncited by 2
  3. 2025Finer Disentanglement of Aleatoric Uncertainty Can Accelerate Chemical Histopathology Imagingcited by 1
  4. 2024Hallucination Detection in Virtually-Stained Histology: A Latent Space Baselinecited by 0
  5. 2025Benchmarking Post-Hoc Unknown-Category Detection in Food Recognitioncited by 0
  6. 2026Anatomy of a failure: When, how, and why deep vision fails in scientific domainscited by 0
  7. 2026Training-Free Out-of-Distribution Detection for Pathology Whole-Slide Imagescited by 0
  8. 2026CHILD: Human-in-the-Loop OOD Detection for Safe Clinical Deploymentcited by 0