The DOI matches its Crossref record; 38 of the 40 references it cites were found in a registry or an index.
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 [57].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [57]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“To improve accessibility, we pre-process the slides into patches and balance the dataset so that each split has a 50/50 class balance, making average accuracy is a reasonable measure of performance (Veeling et al., 2018; Tellez et al., 2019).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Tellez et al., 2019
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measured
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“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
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
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“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
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“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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“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]
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
Statistical Comparisons of Classifiers over Multiple Data Sets
Demšar, J. (2006) · doi:10.5555/1248547.1248548 · Cited by 12,051
(2016) · Cited by 11,679
scikit-image: image processing in Python
(2014) · doi:10.7717/peerj.453 · Cited by 6,794
Research works printed but not linked: 1
- Haeberli, 1994, Image processing by linear interpolation and extrapolation, IRIS Uni. Mag., 28, 8
Cited by
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Items 601–644 of 644
Webers, J., Martens, S., Gervois, P., Mertens, I., Tolkach, Y., Sucaet, Y., Wolfs, E., & Valkenborg, D. (2026) · Histopathology · doi:10.1111/his.70268
姚以才, Yuan, Z., Li, G., Liu, Z., & Yuan, J. (2026) · International Conference on Intelligent Multimedia, Networking, and Security (IMNS) · doi:10.1109/imns67862.2026.11655264
Artificial intelligence (imaging analysis) fundamentals
Akbar, A., & Niazi, M. K. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40383-5.00006-6
Other applications of digital pathology
Rasool, G., Singh, R., & Bui, M. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40383-5.00009-1
Loo, L. Y., Luo, W.-B., Lam, I. H.-K., Heng, S. Y., Cheo, F. F., Huang, J., Song, S. W., Yeoh, T., Feng, F. X., Baharudin, S. N., et al. (2026) · JAAD International · doi:10.1016/j.jdin.2026.08.010
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Daniel, V. O., Coupland, C. A., Sander, N., Mboli, J. S., Calaminus, S. D. J., & Sousa, E. (2026) · Journal of Imaging · doi:10.3390/jimaging12100519
Domain-generalizable oral cancer classification: bridging multi-center histopathological datasets
Hua, Z., & Zhou, Y. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1951350
Sun, S., Yang, H., Wang, H., Du, S., Liu, S., & Lai, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38407-2_46
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Akgün, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1358-8.ch009
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Akgün, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1358-8.ch003
Cross-Species Diagnostic Translation in Volumetric Tissue Profiling
Yemul, R. M., Vyavahare, S. S., Patil, N., & Sagare, P. G. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1358-8.ch010
Nguyen, N. H., Lý, K. N., & Le, N. Q. K. (2026) · Computers · doi:10.3390/computers15090626
Fujisawa, M., Ohara, T., Shimada, Y., Takeuchi, K., Shimayoshi, T., Sugiyama, T., Higuchi, T., Iwasaki, Y., & Matsukawa, A. (2026) · Laboratory Investigation · doi:10.1016/j.labinv.2026.106166
Dik, S. Z., Çağlar, H., Karadeniz, B. S., Aydin, M., & Kuş, Z. (2026) · International Conference on Machine Learning Technologies (ICMLT) · doi:10.1109/icmlt69916.2026.11689086
Iqbal, M. A., Chen, H., Chung, V., Anaissi, A., Alyatimi, A., Shahzadi, H., & Saleem, M. S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-92-0971-2_29
Subramanian, B., Jeyaraj, R., Noh, S., Fisher, G. A., & Shen, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38172-9_62
Yilmaz, G., & Çifçi, M. A. (2026) · Cancers · doi:10.3390/cancers18193098
Niwase, S., & Fujiyama, A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.21.753083
Xu, N., Chen, A., Ruan, H., Yang, D.-L., Liao, R.-J., Qi, X., Fong, S., Cao, D.-D., Yu, L., Huang, Y.-H., et al. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001744
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Filho, J. L. D. S. B., Rios, T. N., & Rios, R. A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-39898-7_38
Cao, C., Ji, Z., He, C., Wang, J., Luo, X., Liu, J., Jing, X., Yan, F., Chen, Y., Dong, Z., et al. (2026) · Advanced Science · doi:10.1002/advs.78019 · Cited by 1
Yilmaz, G., & Çifçi, M. A. (2026) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2026.100750
Parui, S., Rai, R. K., Vishwakarma, P. K., Singh, D. K., & Sahu, A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-39379-1_10
Yoo, S., & Goh, W. W. B. (2026) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2026.111979
The accuracy–utility gap: Why AI accuracy does not guarantee clinical benefit in digital pathology
Battiato, R., Silva, F. M., Morales, M. C., Romero, D. S., & Benites, M. H. (2026) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2026.100751
Gadermayr, M., Tschuchnig, M., Stangassinger, L. M., Kreutzer, C., Couillard‐Després, S., Oostingh, G. J., & Hittmair, A. (2026) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms69103.2026.00018
Du, Y., Bu, Q., Liu, F., Zhang, J., & Deng, Y. (2026) · Electronics · doi:10.3390/electronics15204616
Lee, S., Hong, J., Kim, S., Hwang, Y., Song, J., & Shin, M. (2026) · Applied Sciences · doi:10.3390/app162010067
Kutlu, H. (2026) · Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi · doi:10.54365/adyumbd.2002976
Gulsen, A. (2026) · Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi · doi:10.17341/gazimmfd.1881154
Ebbert, J. L., Perry, A. S., Szymanski, J., & Corte, D. D. (2026) · Uro · doi:10.3390/uro6040028
Rong, Z., Zeng, X., Zhang, J., & Zhang, Q. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111648
Liu, X., Shi, Z., Wang, G., Wang, J., Liu, Y., Gong, Y., Lin, X., Wu, D., Zhuo, S., Han, H., et al. (2026) · Advanced Science · doi:10.1002/advs.78299
Asaduzzaman, Nguyen, H. D., Das, U. K., Chen, C.-I., Chang, C.-J., & Yang, T.-S. (2026) · Pattern Analysis and Applications · doi:10.1007/s10044-026-01795-9
Mohamed, N. A. R. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02551-y
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Treillard, S., Torre, C. D. L., Franchet, C., Luga, H., Wilson, D. G., & Cussat‐Blanc, S. (2026) · Genetic Programming and Evolvable Machines · doi:10.1007/s10710-026-09547-8
Ramin, L., Ramge, J.-M., Neunaber, C., & Wolff, D. (2026) · Scientific Reports · doi:10.1038/s41598-026-76895-x
Athulya, C., Das, D., & Hejmadi, M. (2026) · International Conference on Next-Gen Intelligent Computing and Applications (ICONICA) · doi:10.1109/iconica70605.2026.11736866
Luo, R.-H., Guo, S.-X., & Bocklitz, T. (2026) · Bioengineering · doi:10.3390/bioengineering13111257
Dunenova, G., Sarsembayev, A., Oshibayeva, A., Kyrykbayeva, S., Abdrakhmanova, Z., Omarova, B., Ivankov, A., Ualiyeva, A. Y., Zhakypova, B., Zhagiparova, Z., et al. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1908349
Domain-gap guided generative data augmentation for low-resource cross-domain image retrieval
Feng, Y., Li, G., Togo, R., Maeda, K., Ogawa, T., & Haseyama, M. (2026) · Machine Vision and Applications · doi:10.1007/s00138-026-01944-4
Decision-Linked Multimodal Prediction in Non-Metastatic Rectal Adenocarcinoma
Mwazha, A., Kader, S., Naicker, N., Mhemedi, B., Mazibuko, Z., Mtembu, T., Mallum, A. A., Ramesar, R., & Kløverpris, H. N. (2026) · Current Oncology · doi:10.3390/curroncol33110650
Zhu, M., Aschenaki, Y., Zhang, X., Sali, R., Lin, Z., Dixon, C. M., & Zhang, D. Y. (2026) · BMJ Oncology · doi:10.1136/bmjonc-2026-001208
Ghosheh, M., Hussein, M. M., Mansour, B., Aljorf, D., Abyad, A. M., & Ghosheh, G. (2026) · Global Advances in Oncology and Hematology · doi:10.4103/gaoh.gaoh_12_26
Metadata
- 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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