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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Sorted by citations
Items 201–300 of 644
Gadermayr, M., Koller, L., Tschuchnig, M., Stangassinger, L. M., Kreutzer, C., Couillard‐Després, S., Oostingh, G. J., & Hittmair, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43987-2_46 · Cited by 21
Brain Tumor Classification with Multimodal MR and Pathology Images
Ma, X., & Jia, F. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46643-5_34 · Cited by 21
Generalized Fixation Invariant Nuclei Detection Through Domain Adaptation Based Deep Learning
Valkonen, M., Högnäs, G., Bova, G. S., & Ruusuvuori, P. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3039414 · Cited by 21
Schiele, S., Arndt, T. T., Martin, B., Miller, S., Bauer, S., Banner, B. M., Brendel, E.-M., Schenkirsch, G., Anthuber, M., Huss, R., et al. (2021) · Cancers · doi:10.3390/cancers13092074 · Cited by 22
Giuste, F., Sequeira, R. C., Keerthipati, V., Lais, P. F., Mirzazadeh, A., Mohseni, A., Zhu, Y., Shi, W., Marteau, B. L., Zhong, Y., et al. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104303 · Cited by 20
Bokhorst, J., Nagtegaal, I. D., Zlobec, I., Dawson, H., Sheahan, K. D., Simmer, F., Kirsch, R., Vieth, M., Lugli, A., van der Laak, J. A. W. M., et al. (2023) · Cancers · doi:10.3390/cancers15072079 · Cited by 20
Self-citationAutomating Ground Truth Annotations for Gland Segmentation Through Immunohistochemistry
Kataria, T., Rajamani, S., Ayubi, A. B., Bronner, M. P., Jedrzkiewicz, J., Knudsen, B. S., & Elhabian, S. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100331 · Cited by 21
González, R., Saha, A., Campbell, C. J. V., Nejat, P., Lokker, C., & Norgan, A. P. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100347 · Cited by 24
Area under the curve may hide poor generalisation to external datasets
Kleppe, A. (2022) · ESMO Open · doi:10.1016/j.esmoop.2022.100429 · Cited by 20
Contrastive Learning Based Stain Normalization Across Multiple Tumor in Histopathology
Ke, J., Shen, Y., Liang, X., & Shen, D. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-87237-3_55 · Cited by 28
Hashimoto, N., Hanada, H., Miyoshi, H., Nagaishi, M., Sato, K., Hontani, H., Ohshima, K., & Takeuchi, I. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100359 · Cited by 20
Modified Reinhard Algorithm for Color Normalization of Colorectal Cancer Histopathology Images
Roy, S., Panda, S., & Jangid, M. (2021) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco54536.2021.9616117 · Cited by 19
Pedersen, A., Smistad, E., Rise, T. V., Dale, V. G., Pettersen, H. S., Nordmo, T.-A. S., Bouget, D., Reinertsen, I., & Valla, M. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.971873 · Cited by 21
Franchet, C., Schwob, R., Bataillon, G., Syrykh, C., Péricart, S., Frénois, F.-X., Penault-Llorca, F. M., Lacroix‐Triki, M., Arnould, L., Lemonnier, J., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108130 · Cited by 20
Marini, N., Marchesin, S., Wodziński, M., Caputo, A., Podareanu, D., Guevara, B. C., Boytcheva, S., Vatrano, S., Fraggetta, F., Ciompi, F., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103303 · Cited by 20
Self-citationA Survey of Data Augmentation in Domain Generalization
Zhong, Y., Zhou, W., & Wang, Z. (2025) · Neural Processing Letters · doi:10.1007/s11063-025-11747-9 · Cited by 24
Explainable nucleus classification using Decision Tree Approximation of Learned Embeddings
Amgad, M. T., Atteya, L. A., Hussein, H., Mohammed, K. H., Hafiz, E. O. A., Elsebaie, M. A., Mobadersany, P., Manthey, D., Gutman, D. A., Elfandy, H., et al. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab670 · Cited by 18
Evaluating generic AutoML tools for computational pathology
Schwen, L. O., Schacherer, D. P., Geißler, C., & Homeyer, A. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.100853 · Cited by 21
Tosta, T. A. A., Freitas, A. D., de Faria, P. R., Neves, L. A., Martins, A. S., & do Nascimento, M. Z. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104978 · Cited by 18
Mehrtens, H. A., Kurz, A., Bucher, T.-C., & Brinker, T. J. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102914 · Cited by 27
Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images
Zaffar, I., Jaume, G., Rajpoot, N., & Mahmood, F. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230723 · Cited by 20
WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval
Tabatabaei, Z., Wang, Y., Colomer, A., Moll, J. O., Zhao, Z., & Naranjo, V. (2023) · Bioengineering · doi:10.3390/bioengineering10101144 · Cited by 18
Gonzalez, R., Nejat, P., Saha, A., Campbell, C. J. V., Norgan, A. P., & Lokker, C. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100348 · Cited by 18
Kanwal, N., Fuster, S., Khoraminia, F., Zuiverloon, T. C. M., Rong, C., & Engan, K. (2022) · Multidimensional Signal Processing Workshop · doi:10.1109/ivmsp54334.2022.9816283 · Cited by 17
YOLF-ShipPnet: Improved RetinaNet with Pyramid Vision Transformer
Qiu, Z.-R., Rong, S.-Y., & Ye, L.-K. (2023) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-023-00235-4 · Cited by 14
Elforaici, M. E. A., Montagnon, E., Romero, F., Le, W., Azzi, F., Trudel, D., Nguyen, B., Turcotte, S., Tang, A., & Kadoury, S. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103346 · Cited by 19
Wies, C., Schneider, L., Haggenmüller, S., Bucher, T.-C., Hobelsberger, S., Heppt, M. V., Ferrara, G., Krieghoff-Henning, E. I., & Brinker, T. J. (2024) · PLOS ONE · doi:10.1371/journal.pone.0297146 · Cited by 16
Deep learning for digital pathology: A critical overview of methodological framework
Rad, M. S., Huang, J., Hosseini, M. M., Choudhary, R., Siezen, H., Akabari, R. R., Jamaspishvili, T., El-Zammar, O. A., Patel, P. G., Carello, S. J., et al. (2025) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2025.100514 · Cited by 22
H&E Multi-Laboratory Staining Variance Exploration with Machine Learning
Prezja, F., Pölönen, I., Äyrämö, S., Ruusuvuori, P., & Kuopio, T. (2022) · Applied Sciences · doi:10.3390/app12157511 · Cited by 16
OralEpitheliumDB: A Dataset for Oral Epithelial Dysplasia Image Segmentation and Classification
Silva, A. B., Martins, A. S., Tosta, T. A. A., Loyola, A. M., Cardoso, S. V., Neves, L. A., de Faria, P. R., & do Nascimento, M. Z. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01041-w · Cited by 16
Rudmann, D. G., Albretsen, J. C., Doolan, C., Gregson, M., Dray, B. K., Sargeant, A. M., O'Shea, D. G., Kuklyte, J., Power, A., & Fitzgerald, J. (2020) · Toxicologic Pathology · doi:10.1177/0192623320973986 · Cited by 16
Marini, N., Atzori, M., Otálora, S., Marchand‐Maillet, S., & Müller, H. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00073 · Cited by 16
Kabir, S., Sarmun, R., Saady, R. M. A., Vranić, S., Murugappan, M., & Chowdhury, M. E. H. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01429-2 · Cited by 17
Convolutional Neural Networks for Chagas’ Parasite Detection in Histopathological Images
Sanchez-Patino, N., Toriz-Vázquez, A., Montiel, N. H., & Pérez-González, J. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629563 · Cited by 15
A systematic comparison of deep learning methods for Gleason grading and scoring
Dominguez-Morales, J. P., Durán-López, L., Marini, N., Vicente-Díaz, S., Linares-Barranco, A., Atzori, M., & Müller, H. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103191 · Cited by 17
Benchmarking PathCLIP for Pathology Image Analysis
Zheng, S., Cui, X., Sun, Y., Li, J., Li, H., Zhang, Y., Chen, P., Jing, X., Ye, Z., & Yang, L. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01128-4 · Cited by 15
Tang, K., Jiang, Z., Wu, K., Shi, J., Xie, F., Wang, W., Wu, H., & Zheng, Y. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3447672 · Cited by 17
StyPath: Style-Transfer Data Augmentation for Robust Histology Image Classification
Cicalese, P. A., Mobiny, A., Yuan, P., Becker, J. U., Mohan, C., & Nguyen, H. V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59722-1_34 · Cited by 15
Frewing, A., Gibson, A. B., Robertson, R., Urie, P. M., & Corte, D. D. (2023) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2022-0460-ra · Cited by 17
Giammanco, A., Bychkov, A., Schallenberg, S., Tsvetkov, T., Fukuoka, J., Pryalukhin, A., Mairinger, F. D., Seper, A., Hulla, W., Klein, S., et al. (2024) · Modern Pathology · doi:10.1016/j.modpat.2024.100496 · Cited by 15
Elazab, N., Soliman, H., El–Sappagh, S., Islam, S. M. R., & Elmogy, M. M. (2020) · RePEc: Research Papers in Economics · Cited by 14
Patterson, N. H., Pingry, E. L., Moser, F. A., Kruse, A. R. S., Dufresne, M., Djambazova, K., Esselman, A. B., Brewer, M., Ardenne, J. M. V., Allen, J. L., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.16.452703 · Cited by 12
Training State-of-the-Art Pathology Foundation Models with Orders of Magnitude Less Data
Karasikov, M., van Doorn, J., Känzig, N., Cesur, M. E., Horlings, H. M., Berke, R., Tang, F., & Otálora, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04984-1_55 · Cited by 13
Huang, Y., Zhao, W., Zhang, Z., Chen, Y., Fu, Y., Wu, F., Jiang, Y., Liang, L., Wang, S., & Yu, L. (2025) · Nature Communications · doi:10.1038/s41467-025-66300-y · Cited by 22
Wang, C., Li, S., Ke, J., Zhang, C., & Shen, Y. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3379280 · Cited by 13
de Bel, T., Litjens, G. J. S., Ogony, J. W., Stallings‐Mann, M. L., Carter, J. M., Hilton, T. W., Radisky, D. C., Vierkant, R. A., Broderick, B. T., Hoskin, T. L., et al. (2022) · npj Breast Cancer · doi:10.1038/s41523-021-00378-7 · Cited by 13
Self-citationHistokt: Cross Knowledge Transfer in Computational Pathology
Zhang, R., Zhu, J., Yang, S. C., Hosseini, M. S., Genovese, A., Chen, L., Rowsell, C. H., Damaskinos, S., Varma, S., & Plataniotis, K. N. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747400 · Cited by 13
Tackling stain variability using CycleGAN-based stain augmentation
Bouteldja, N., Hölscher, D. L., Bülow, R. D., Roberts, I. S. D., Coppo, R., & Boor, P. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100140 · Cited by 14
Scalbert, M., Vakalopoulou, M., & Couzinié-Devy, F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16434-7_12 · Cited by 13
Automatic Classification of Multiple Objects in Automotive Assembly Line
Mazzetto, M., Southier, L. F. P., Teixeira, M., & Casanova, D. (2019) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa.2019.8869063 · Cited by 14
Pohlmeyer-Esch, G., Halsey, C., Boisclair, J., Ram, S., Kitz, S., Knight, B., Moulin, P., & Frisk, A.-L. (2025) · Toxicologic Pathology · doi:10.1177/01926233251340622 · Cited by 13
Deep Neural Network-Aided Histopathological Analysis of Myocardial Injury
Jiao, Y., Yuan, J., Sodimu, O. M., Qiang, Y., & Ding, Y. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.724183 · Cited by 12
Terada, Y., Takahashi, T., Hayakawa, T., Ono, A., Kawata, T., Isaka, M., Muramatsu, K., Tone, K., Kodama, H., Imai, T., et al. (2022) · JCO Clinical Cancer Informatics · doi:10.1200/cci.22.00070 · Cited by 12
Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer
Mittmann, G., Laiouar-Pedari, S., Mehrtens, H. A., Haggenmüller, S., Bucher, T.-C., Chanda, T., Gaisa, N. T., Wagner, M., Klamminger, G. G., Rau, T. T., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64712-4 · Cited by 15
An unsupervised style normalization method for cytopathology images
Chen, X., Yu, J., Cheng, S., Geng, X., Liu, S., Han, W., Hu, J., Chen, L., Liu, X., & Zeng, S. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.06.025 · Cited by 12
Wang, C., Huang, S.-C., Khalil, M.-A., Hong, D.-Z., Meng, S.-I., & Lee, Y. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad344 · Cited by 12
On Transferability of Histological Tissue Labels in Computational Pathology
Hosseini, M. S., Chan, L., Huang, W., Wang, Y., Hasan, D., Rowsell, C. H., Damaskinos, S., & Plataniotis, K. N. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58526-6_27 · Cited by 12
HistomicsTK: A Python toolkit for pathology image analysis algorithms
Pourakpour, F., Szölgyén, Á., Nateghi, R., Gutman, D. A., Manthey, D., & Cooper, L. (2025) · SoftwareX · doi:10.1016/j.softx.2025.102318 · Cited by 11
Hybrid CNN-Transformer Models in Histopathology Image Analysis: A Scoping Review
Oliveira, D. L. L., Tosta, T. A. A., Neves, L. A., & do Nascimento, M. Z. (2025) · IEEE Access · doi:10.1109/access.2025.3644767 · Cited by 11
Ameen, Y. A., Badary, D. M., Abonnoor, A. E. I., Hussain, K. F., & Sewisy, A. A. E.-M. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05199-y · Cited by 14
Tran, M. H., Ma, L., Mubarak, H. K., Gomez, O., Yu, J., Bryarly, M. D., & Fei, B. (2024) · Journal of Biomedical Optics · doi:10.1117/1.jbo.29.9.093505 · Cited by 11
Sani, M. M., Royat, A., & Baghshah, M. S. (2024) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-024-03224-7 · Cited by 12
Wang, R., Gunesli, G. N., Skingen, V. E., Valen, K.-A. F., Lyng, H., Young, L. S., & Rajpoot, N. (2025) · npj Precision Oncology · doi:10.1038/s41698-024-00778-5 · Cited by 11
Deeply supervised two stage generative adversarial network for stain normalization
Du, Z., Zhang, P., Huang, X., Hu, Z., Yang, G., Xi, M., & Liu, D. (2025) · Scientific Reports · doi:10.1038/s41598-025-91587-8 · Cited by 11
Tuly, S. R., Ranjbari, S., Murat, E. A., & Arslanturk, S. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110108 · Cited by 13
NCT-CRC-HE: Not All Histopathological Datasets are Equally Useful
Ignatov, A., & Malivenko, G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91721-9_19 · Cited by 13
AI for colon cancer: A focus on classification, detection, and predictive modeling
Merabet, A., Saighi, A., Harous, S., Ferradji, M. A., Laboudi, Z., Almaktoom, A. T., Mousavirad, S. J., Elbatal, I., & Mohamed, A. W. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106115 · Cited by 19
Brodsky, V., Levine, L., Solans, E. P., Dola, S., Chervony, L., & Polak, S. (2022) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2021-0550-oa · Cited by 11
Multicenter automatic detection of invasive carcinoma on breast whole slide images
Peyret, R., Pozin, N., Sockeel, S., Kammerer‐Jacquet, S., Adam, J., Bocciarelli, C., Ditchi, Y., Bontoux, C., Depoilly, T., Guichard, L., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000091 · Cited by 11
Somaratne, U. V., Wong, K. W., Parry, J., & Laga, H. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08810-8 · Cited by 12
Deep Learning Prediction of TERT Promoter Mutation Status in Thyroid Cancer Using Histologic Images
Kim, J., Ko, S., Kim, M., Park, J. Y., Han, H. S., Cho, J., & Park, J. Y. (2023) · Medicina · doi:10.3390/medicina59030536 · Cited by 12
Ren, T., Govindarajan, V., Bourouis, S., Wang, X., & Ke, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-15702-5 · Cited by 12
A Laplacian Pyramid Based Generative H&E Stain Augmentation Network
Li, F., Hu, Z., Chen, W., & Kak, A. C. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3317239 · Cited by 10
Fang, T., Wu, Z., Chen, X., Tan, L., Li, Z., Ji, J., Fan, Y., Li, Z., & Yue, S. (2024) · Advanced Intelligent Systems · doi:10.1002/aisy.202300689 · Cited by 10
Automated multi-scale computational pathotyping (AMSCP) of inflamed synovial tissue
Bell, R. D., Brendel, M., Konnaris, M. A., Xiang, J., Otero, M., Fontana, M. A., Bai, Z., Albrecht, J., Apruzzese, W. A., Boyce, B. F., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51012-6 · Cited by 12
Artificial intelligence for pathology
Xing, F., Zhang, X., & Cornish, T. C. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821259-2.00011-9 · Cited by 13
Gehlot, S., & Gupta, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87589-3_49 · Cited by 10
Spectral decoupling for training transferable neural networks in medical imaging
Pohjonen, J., Stürenberg, C., Rannikko, A. S., Mirtti, T. K., & Pitkänen, E. (2022) · iScience · doi:10.1016/j.isci.2022.103767 · Cited by 10
Staining condition visualization in digital histopathological whole-slide images
Jiao, Y., Li, J., & Fei, S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12559-y · Cited by 10
Analysing cerebrospinal fluid with explainable deep learning: From diagnostics to insights
Schweizer, L., Seegerer, P., Kim, H., Saitenmacher, R., Muench, A., Barnick, L., Osterloh, A., Dittmayer, C., Jödicke, R., Pehl, D., et al. (2022) · Neuropathology and Applied Neurobiology · doi:10.1111/nan.12866 · Cited by 10
Ruiz, F. M., Lahrmann, B., Bartels, L., Krauthoff, A., Keil, A., Härtel, S., Tao, A. S., Ströbel, P., Clarke, M. A., Wentzensen, N., et al. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1173616 · Cited by 10
Multi-class Tissue Classification in Colorectal Cancer with Handcrafted and Deep Features
Altini, N., Marvulli, T. M., Caputo, M., Mattioli, E., Prencipe, B., Cascarano, G. D., Brunetti, A., Tommasi, S., Bevilacqua, V., Summa, S. D., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84522-3_42 · Cited by 10
McCombe, K. D., Craig, S. G., Pulsawatdi, A. V., Quezada-Marín, J. I., Hagan, M., Rajendran, S., Humphries, M. P., Bingham, V., Salto‐Tellez, M., Gault, R., et al. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.08.033 · Cited by 10
Mahmood, T., Kim, S. G., Koo, J. H., & Park, K. R. (2022) · Mathematics · doi:10.3390/math10111909 · Cited by 10
A video transformer network for thyroid cancer detection on hyperspectral histologic images
Tran, M. H., Gomez, O., & Fei, B. (2023) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2654851 · Cited by 10
Analysis of cellularity in H&E-stained rat bone marrow tissue via deep learning
Shiffman, S., Piedra, E. A. R., Adedeji, A. O., Ruff, C. F., Andrews, R. N., Katavolos, P., Liu, E., Forster, A., Brumm, J., Fuji, R. N., et al. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100333 · Cited by 9
Höppener, D. J., Aswolinskiy, W., Qian, Z., Tellez, D., Nierop, P. M. H., Starmans, M. P. A., Nagtegaal, I. D., Doukas, M., de Wilt, J. H. W., Grünhagen, D. J., et al. (2024) · BJS Open · doi:10.1093/bjsopen/zrae127 · Cited by 11
Self-citationFrascarelli, C., Venetis, K., Marra, A., Concardi, A., D’Ercole, M., Mangione, E., Negrelli, M., Porta, F. M., Keswani, S., Curigliano, G., et al. (2025) · npj Breast Cancer · doi:10.1038/s41523-025-00857-1 · Cited by 12
Hussain, M. A., Waris, A., Akram, M. U., Khan, M. J., Asaf, M. Z., Javaid, A., Gilani, S. O., & Hazzazi, F. (2026) · PLoS ONE · doi:10.1371/journal.pone.0341311 · Cited by 10
Pham, T. T. A., Kim, H., Lee, Y., Kang, H. W., & Park, S. (2022) · IEEE Access · doi:10.1109/access.2022.3162272 · Cited by 9
Nerrienet, N., Peyret, R., Sockeel, M., & Sockeel, S. (2023) · Journal of medical imaging · doi:10.1117/1.jmi.10.6.067502 · Cited by 9
Xu, Y. L., Jiang, L., Huang, S., Liu, Z., & Zhang, J. (2022) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2021-208042 · Cited by 9
Towards Measuring Domain Shift in Histopathological Stain Translation in an Unsupervised Manner
Nisar, Z., Vasiljević, J., Gançarski, P., & Lampert, T. A. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761411 · Cited by 9
General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology
Makhlouf, Y., Salto‐Tellez, M., James, J. A., O’Reilly, P. G., & Maxwell, P. (2022) · Diagnostics · doi:10.3390/diagnostics12051272 · Cited by 9
Test Time Transform Prediction for Open Set Histopathological Image Recognition
Galdrán, A., Hewitt, K. J., Laleh, N. G., Kather, J. N., Carneiro, G., & Ballester, M. Á. G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16434-7_26 · Cited by 9
A detection method for small casting defects based on bidirectional feature extraction
Zhang, S., Li, H., Ren, P., Peng, T., & Meng, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-90185-y · Cited by 8
Geometric multi-instance learning for weakly supervised gastric cancer segmentation
Huang, C., Xia, H., Xiao, X., Hong, C. H. E. N., Jiang, Y., Lyu, Y., Ni, Z., Wang, T., Wang, N. 王., & Huang, Q. (2026) · npj Digital Medicine · doi:10.1038/s41746-025-02287-6 · Cited by 9
Akhyar, F., Lin, C., & Kathiresan, G. S. (2021) · International Conference on Multimedia Retrieval · doi:10.1145/3460426.3463666 · Cited by 8
Suspicious Region Segmentation Using Deep Features in Breast Cancer Mammogram Images
Zebari, D. A., Ibrahim, D. A., & Al-Zebari, A. (2022) · doi:10.1109/csase51777.2022.9759633 · Cited by 8
Kotte, S., Saipradeep, V. G., Sivadasan, N., Joseph, T., Sharma, H., Walia, V., Varma, B., & Mukherjee, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-33658-4_23 · Cited by 8
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
