Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
Open accessAbstract
Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides that exhibit similar but not identical color appearance. Due to this color shift between laboratories, convolutional neural networks (CNNs) trained with images from one lab often underperform on unseen images from the other lab. Several techniques have been proposed to reduce the generalization error, mainly grouped into two categories: stain color augmentation and stain color normalization. The former simulates a wide variety of realistic stain variations during training, producing stain-invariant CNNs. The latter aims to match training and test color distributions in order to reduce stain variation. For the first time, we compared some of these techniques and quantified their effect on CNN classification performance using a heterogeneous dataset of hematoxylin and eosin histopathology images from 4 organs and 9 pathology laboratories. Additionally, we propose a novel unsupervised method to perform stain color normalization using a neural network. Based on our experimental results, we provide practical guidelines on how to use stain color augmentation and stain color normalization in future computational pathology applications.
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Techniques such as rotation, scaling, color normalization, or stain augmentation help simulate variability encountered in real-world slides.
“Techniques such as rotation, scaling, color normalization, or stain augmentation help simulate variability encountered in real-world slides [57].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [57]
Section 1.1 says data augmentation simulates realistic variations and lists rotation and scaling; it also says color augmentation simulates stain variation. Section 3.2 distinguishes color normalization: it reduces variation by matching training and test color distributions rather than simulating variability. (LLM, conf=0.88)
Experimental — accuracy not yet measuredIn histopathology, variation between hospitals can arise from differences in the patient population or in slide staining and image acquisition.
“In histopathology applications-studying tissue slides under a microscope-this variation can arise from sources like differences in the patient population or in slide staining and image acquisition (Veta et al., 2016; Komura and Ishikawa, 2018; Tellez et al., 2019).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Tellez et al., 2019
The Introduction describes stain-color differences between laboratories, and §2.3 describes slides with different staining and scanning conditions. Our copy does not identify differences in patient populations as a source of between-hospital variation, so that part of the cited claim is unsupported. (LLM, conf=0.88)
Experimental — accuracy not yet measuredRegulatory frameworks for AI in oncology remain evolving, with significant gaps in liability assignment when AI-assisted decisions lead to adverse outcomes.
“Beyond technical challenges, regulatory frameworks for AI in oncology remain evolving, with significant gaps in liability assignment when AI-assisted decisions lead to adverse outcomes [124].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [124]
Prior work suggests that staining differences are the main source of variation between hospitals in similar datasets.
“Prior work suggests that staining differences are the main source of variation between hospitals in similar datasets (Tellez et al., 2019).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Tellez et al., 2019
Specialized stain augmentation methods can close the in- and out-of-distribution accuracy gap on a variant of the dataset based on the same underlying slides.
“specialized stain augmentation methods can close the in- and out-of-distribution accuracy gap on a variant of the dataset based on the same underlying slides (Tellez et al., 2019)”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Tellez et al., 2019
Differences in staining between hospitals are the primary source of variation in the Camelyon17-wilds dataset.
“Prior work has shown that differences in staining between hospitals are the primary source of variation in this dataset”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Tellez et al., 2019
These specialized methods are reasonably effective at mitigating stain variation, at least in some contexts.
“These methods are reasonably effective at mitigating stain variation, at least in some contexts (Tellez et al., 2019; Miller et al., 2021)”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Tellez et al., 2019
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- Authors
- Tellez, D., Litjens, G., Bándi, P., Bulten, W., Bokhorst, J.-M., Ciompi, F., & van der Laak, J.
- Affiliations
- Radboud University Medical Center (NL)
- Corresponding author
- Tellez, David
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2019-12
- Volume
- 58
- Pages
- 101544
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W2969278648
- Cited by
- 649
- References
- 40
- Citations by year
- 2026: 142 · 2025: 108 · 2024: 80 · 2023: 92 · 2022: 89 · 2021: 81 · 2020: 42 · 2019: 14
- FWCI
- 25.50
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- KWF Kankerbestrijding
- Radboud Universitair Medisch Centrum
- Horizon 2020
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- computational pathology, convolutional neural networks, histopathological image classification
- MeSH terms
- Unsupervised Machine Learning, Color, Eosine Yellowish-(YS), Hematoxylin, Humans, Pathology, Clinical, Staining and Labeling, Neural Networks, Computer, Datasets as Topic
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