mlsanityData to 7 Oct 2026

Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology

arXiv 2206.14973 ↗First posted 30 Jun 2022cs.CV cs.AIcited by 26

Not on the map. Why?

Abstract (arXiv)

When designing a diagnostic model for a clinical application, it is crucial to guarantee the robustness of the model with respect to a wide range of image corruptions. Herein, an easy-to-use benchmark is established to evaluate how deep neural networks perform on corrupted pathology images. Specifically, corrupted images are generated by injecting nine types of common corruptions into validation images. Besides, two classification and one ranking metrics are designed to evaluate the prediction and confidence performance under corruption. Evaluated on two resulting benchmark datasets, we find that (1) a variety of deep neural network models suffer from a significant accuracy decrease (double the error on clean images) and the unreliable confidence estimation on corrupted images; (2) A low correlation between the validation and test errors while replacing the validation set with our benchmark can increase the correlation. Our codes are available on https://github.com/superjamessyx/robustness_benchmark.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 22 of its 34 references that are in the map. First posted on arXiv 30 Jun 2022.

Builds on

22 of 34 references.

References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  5. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  6. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  7. 2013Intriguing properties of neural networkscited by 5,114
  8. 2017On Calibration of Modern Neural Networkscited by 4,860
  9. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  10. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  11. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,857
  12. 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,430
10 more
  1. 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,194
  2. 2018Rotation Equivariant CNNs for Digital Pathologycited by 420
  3. 2019Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Comingcited by 332
  4. 2018Why do deep convolutional networks generalize so poorly to small image transformations?cited by 198
  5. 2021Are Transformers More Robust Than CNNs?cited by 187
  6. 2016Understanding How Image Quality Affects Deep Neural Networkscited by 118
  7. 2019Benchmarking the Robustness of Semantic Segmentation Modelscited by 111
  8. 2021When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarkscited by 41
  9. 2021Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentationcited by 26
  10. 2021Benchmarks for Corruption Invariant Person Re-identificationcited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 9; 2024: 10; 2025: 4; 2026: 3.

26 papers.

Sort:

By area:

  1. 2023Attention-Challenging Multiple Instance Learning for Whole Slide Image Classificationcited by 61
  2. 2023Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classificationcited by 54
  3. 2024PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathologycited by 43
  4. 2023Domain Generalization in Computational Pathology: Survey and Guidelinescited by 29
  5. 2024MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptionscited by 17
  6. 2024A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid Transformer and Contrastive Learningcited by 13
  7. 2024Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysiscited by 12
  8. 2024AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image Classificationcited by 10
  9. 2024Are We Ready for Out-of-Distribution Detection in Digital Pathology?cited by 8
  10. 2024From Correlation to Causation: Max-Pooling-Based Multi-Instance Learning Leads to More Robust Whole Slide Image Classificationcited by 7
  11. 2023Artifact Restoration in Histology Images with Diffusion Probabilistic Modelscited by 6
  12. 2023Assessing and Enhancing Robustness of Deep Learning Models with Corruption Emulation in Digital Pathologycited by 6
14 more
  1. 2024Benchmarking PathCLIP for Pathology Image Analysiscited by 6
  2. 2023Understanding Silent Failures in Medical Image Classificationcited by 5
  3. 2025Medical Image Classification with KAN-Integrated Transformers and Dilated Neighborhood Attentioncited by 5
  4. 2023Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Imagescited by 3
  5. 2023Long-MIL: Scaling Long Contextual Multiple Instance Learning for Histopathology Whole Slide Image Analysiscited by 3
  6. 2026Patch-to-Global: Random Patch Diffusion for Globally Consistent Megapixel Artifact Inpainting in Whole Slide Imagescited by 1
  7. 2023Artifact-Robust Graph-Based Learning in Digital Pathologycited by 0
  8. 2024Adversary-Robust Graph-Based Learning of WSIscited by 0
  9. 2024Hallucination Detection in Virtually-Stained Histology: A Latent Space Baselinecited by 0
  10. 2025Domain Adaptation via Feature Refinementcited by 0
  11. 2025Joint Deblurring and 3D Reconstruction for Macrophotographycited by 0
  12. 2025Simple Agents Outperform Experts in Biomedical Imaging Workflow Optimizationcited by 0
  13. 2026MedQ-Deg: A Multidimensional Benchmark for Evaluating MLLMs Across Medical Image Quality Degradationscited by 0
  14. 2026Discontinuous Galerkin Neural Operator for Pathology Defocus Deblurringcited by 0