mlsanityData to 14 Oct 2026

Staining normalization in histopathology: Method benchmarking using multicenter dataset

arXiv 2506.19106 ↗First posted 23 Jun 2025eess.IV cs.CV q-bio.TOcited by 0

Not on the map. Why?

Abstract (arXiv)

Hematoxylin and Eosin (H&E) has been the gold standard in tissue analysis for decades, however, tissue specimens stained in different laboratories vary, often significantly, in appearance. This variation poses a challenge for both pathologists' and AI-based downstream analysis. Minimizing stain variation computationally is an active area of research. To further investigate this problem, we collected a unique multi-center tissue image dataset, wherein tissue samples from colon, kidney, and skin tissue blocks were distributed to 66 different labs for routine H&E staining. To isolate staining variation, other factors affecting the tissue appearance were kept constant. Further, we used this tissue image dataset to compare the performance of eight different stain normalization methods, including four traditional methods, namely, histogram matching, Macenko, Vahadane, and Reinhard normalization, and two deep learning-based methods namely CycleGAN and Pixp2pix, both with two variants each. We used both quantitative and qualitative evaluation to assess the performance of these methods. The dataset's inter-laboratory staining variation could also guide strategies to improve model generalizability through varied training data

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 61 references that are in the map. First posted on arXiv 23 Jun 2025.

Builds on

15 of 61 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  4. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  5. 2019Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathologycited by 173
  6. 2024Multimodal Whole Slide Foundation Model for Pathologycited by 162
  7. 2018Methods for Segmentation and Classification of Digital Microscopy Tissue Imagescited by 73
  8. 2018StainGAN: Stain Style Transfer for Digital Histological Imagescited by 68
  9. 2019Deep learning-based survival prediction for multiple cancer types using histopathology imagescited by 47
  10. 2023Domain Generalization in Computational Pathology: Survey and Guidelinescited by 29
  11. 2020Pix2Pix-based Stain-to-Stain Translation: A Solution for Robust Stain Normalization in Histopathology Images Analysiscited by 23
  12. 2015Computational Pathology: Challenges and Promises for Tissue Analysiscited by 20
3 more
  1. 2017Neural Stain-Style Transfer Learning using GAN for Histopathological Imagescited by 18
  2. 2023Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosiscited by 4
  3. 2023Generative Adversarial Networks for Stain Normalisation in Histopathologycited by 3

Built on by

Not cited yet.