mlsanityData to 7 Oct 2026

Towards Measuring Domain Shift in Histopathological Stain Translation in an Unsupervised Manner

arXiv 2205.04368 ↗First posted 9 May 2022eess.IV cs.CVcited by 3

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Abstract (arXiv)

Domain shift in digital histopathology can occur when different stains or scanners are used, during stain translation, etc. A deep neural network trained on source data may not generalise well to data that has undergone some domain shift. An important step towards being robust to domain shift is the ability to detect and measure it. This article demonstrates that the PixelCNN and domain shift metric can be used to detect and quantify domain shift in digital histopathology, and they demonstrate a strong correlation with generalisation performance. These findings pave the way for a mechanism to infer the average performance of a model (trained on source data) on unseen and unlabelled target data.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 14 of its 24 references that are in the map. First posted on arXiv 9 May 2022.

Builds on

14 of 24 references.

References · 14

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  3. 2017PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modificationscited by 415
  4. 2017PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examplescited by 243
  5. 2019Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathologycited by 172
  6. 2015On Wasserstein Two Sample Testing and Related Families of Nonparametric Testscited by 165
  7. 2019Deep neural network models for computational histopathology: A surveycited by 165
  8. 2018StainGAN: Stain Style Transfer for Digital Histological Imagescited by 68
  9. 2019Adversarial Self-Defense for Cycle-Consistent GANscited by 20
  10. 2020Towards Histopathological Stain Invariance by Unsupervised Domain Augmentation using Generative Adversarial Networkscited by 17
  11. 2020Virtual staining for mitosis detection in Breast Histopathologycited by 13
  12. 2018Strategies for Training Stain Invariant CNNscited by 7
2 more
  1. 2018Virtualization of tissue staining in digital pathology using an unsupervised deep learning approachcited by 6
  2. 2021Self adversarial attack as an augmentation method for immunohistochemical stainingscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024Resource Efficient Multi-stain Kidney Glomeruli Segmentation via Self-supervisioncited by 2
  2. 2025UWarp: A Whole Slide Image Registration Pipeline to Characterize Scanner-Induced Local Domain Shiftcited by 0
  3. 2026DSA-CycleGAN: A Domain Shift Aware CycleGAN for Robust Multi-Stain Glomeruli Segmentationcited by 0