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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.
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“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.
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“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.
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“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.
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“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
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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
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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
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van der Laak, J., Litjens, G., & Ciompi, F. (2021) · Nature Medicine · doi:10.1038/s41591-021-01343-4 · Cited by 902
Self-citationDeep neural network models for computational histopathology: A survey
Srinidhi, C. L., Ciga, O., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101813 · Cited by 749
Transformer-based unsupervised contrastive learning for histopathological image classification
Wang, X., Yang, S., Zhang, J., Wang, M., Zhang, J., Yang, W., Huang, J., & Han, X. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102559 · Cited by 751
Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis
Fu, Y., Jung, A. W., Viñas, R., González, S., Vöhringer, H. S., Shmatko, A., Yates, L. R., Jimenez‐Liñan, M., Moore, L., & Gerstung, M. (2020) · Nature Cancer · doi:10.1038/s43018-020-0085-8 · Cited by 655
A Multi-Organ Nucleus Segmentation Challenge
Kumar, N., Verma, R., Anand, D., Zhou, Y., Onder, O. F., Tsougenis, E., Chen, H., Heng, P., Li, J., Hu, Z., et al. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2947628 · Cited by 583
Yamashita, R., Long, J., Longacre, T., Peng, L., Berry, G., Martin, B., Higgins, J., Rubin, D. L., & Shen, J. (2021) · The Lancet Oncology · doi:10.1016/s1470-2045(20)30535-0 · Cited by 391
Salvi, M., Acharya, U. R., Molinari, F., & Meiburger, K. M. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104129 · Cited by 420
Artificial intelligence for digital and computational pathology
Song, A. H., Jaume, G., Williamson, D. F. K., Lu, M. Y., Vaidya, A., Miller, T. R., & Mahmood, F. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00096-8 · Cited by 356
Measuring Domain Shift for Deep Learning in Histopathology
Stacke, K., Eilertsen, G., Unger, J., & Lundström, C. F. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3032060 · Cited by 365
The impact of site-specific digital histology signatures on deep learning model accuracy and bias
Howard, F. M., Dolezal, J., Kochanny, S., Schulte, J., Chen, H., Heij, L., Huo, D., Nanda, R., Olopade, O. I., Kather, J. N., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24698-1 · Cited by 330
Designing deep learning studies in cancer diagnostics
Kleppe, A., Skrede, O.-J., Raedt, S. D., Liestøl, K., Kerr, D. J., & Danielsen, H. E. (2021) · Nature reviews. Cancer · doi:10.1038/s41568-020-00327-9 · Cited by 328
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518
“The Camelyon17-wilds patch-based dataset is similar to one of the datasets used in Tellez et al. (2019), which was also derived from the CAMELYON17 challenge; there, only one hospital is used as the training set, and the other hospitals are all part of the test set.”
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Matek, C., Krappe, S., Münzenmayer, C., Haferlach, T., & Marr, C. (2021) · Blood · doi:10.1182/blood.2020010568 · Cited by 193
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Wang, X., Yang, S., Zhang, J., Wang, M., Zhang, J., Huang, J., Yang, W., & Han, X. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-87237-3_18 · Cited by 178
Amgad, M. T., Stovgaard, E. S., Balslev, E., Thagaard, J., Chen, W., Dudgeon, S. N., Sharma, A., Kerner, J. K., Denkert, C., Yuan, Y., et al. (2020) · npj Breast Cancer · doi:10.1038/s41523-020-0154-2 · Cited by 170
Self-citationLin, J., Chen, Y.-A., Campton, D. E., Cooper, J., Coy, S., Yapp, C., Tefft, J. B., McCarty, E. F., Ligon, K. L., Rodig, S. J., et al. (2023) · Nature Cancer · doi:10.1038/s43018-023-00576-1 · Cited by 180
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Pati, P., Jaume, G., Foncubierta–Rodríguez, A., Feroce, F., Anniciello, A. M., Scognamiglio, G., Brancati, N., Fiche, M., Dubruc, E., Riccio, D., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102264 · Cited by 185
MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography
Lamm, L., Zufferey, S., Zhang, H., Righetto, R. D., Waltz, F., Wietrzyñski, W., Yamauchi, K. A., Burt, A., Liu, Y., Martínez-Sánchez, A., et al. (2026) · Nature Methods · doi:10.1038/s41592-026-03178-8 · Cited by 179
Quality control stress test for deep learning-based diagnostic model in digital pathology
Schömig-Markiefka, B., Pryalukhin, A., Hulla, W., Bychkov, A., Fukuoka, J., Madabhushi, A., Achter, V., Nieroda, L., Büttner, R., Quaas, A., et al. (2021) · Modern Pathology · doi:10.1038/s41379-021-00859-x · Cited by 146
Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling
Filiot, A., Ghermi, R., Olivier, A., Jacob, P., Fidon, L., Camara, A., Mac Kain, A., Saillard, C., & Schiratti, J.-B. (2023) · doi:10.1101/2023.07.21.23292757 · Cited by 148
Hoque, M. Z., Keskinarkaus, A., Nyberg, P., & Seppänen, T. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101997 · Cited by 153
Generative models improve fairness of medical classifiers under distribution shifts
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Beeravolu, A. R., Azam, S., Jonkman, M., Shanmugam, B., Kannoorpatti, K., & Anwar, A. (2021) · IEEE Access · doi:10.1109/access.2021.3058773 · Cited by 131
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Hosseini, M. S., Bejnordi, B. E., Trinh, V. Q., Chan, L., Hasan, D., Li, X., Yang, S. C., Kim, T., Zhang, H., Wu, T., et al. (2024) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100357 · Cited by 142
StainNet: A Fast and Robust Stain Normalization Network
Kang, H., Luo, D., Feng, W., Zeng, S., Quan, T., Hu, J., & Liu, X. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.746307 · Cited by 123
Self-supervised driven consistency training for annotation efficient histopathology image analysis
Srinidhi, C. L., Kim, S. W., Chen, F.-D., & Martel, A. L. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102256 · Cited by 116
Ianni, J. D., Soans, R. E., Sankarapandian, S., Chamarthi, R. V., Ayyagari, D., Olsen, T. G., Bonham, M. J., Stavish, C. C., Motaparthi, K., Cockerell, C. J., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-59985-2 · Cited by 117
Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential
Tschuchnig, M., Oostingh, G. J., & Gadermayr, M. (2020) · Patterns · doi:10.1016/j.patter.2020.100089 · Cited by 106
A High-Performance System for Robust Stain Normalization of Whole-Slide Images in Histopathology
Anghel, A., Stanisavljević, M., Andani, S., Papandreou, N., Rüschoff, J. H., Wild, P. J., Gabrani, M., & Pozidis, H. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00193 · Cited by 106
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Steyaert, S., Qiu, Y. L., Zheng, Y., Mukherjee, P., Vogel, H. S., & Gevaert, O. (2023) · Communications Medicine · doi:10.1038/s43856-023-00276-y · Cited by 120
Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers
Caldéraro, J., & Kather, J. N. (2020) · Gut · doi:10.1136/gutjnl-2020-322880 · Cited by 108
Residual cyclegan for robust domain transformation of histopathological tissue slides
de Bel, T., Bokhorst, J., van der Laak, J. A. W. M., & Litjens, G. J. S. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102004 · Cited by 95
Self-citationKanwal, N., Pérez-Bueno, F., Schmidt, A., Engan, K., & Molina, R. (2022) · IEEE Access · doi:10.1109/access.2022.3176091 · Cited by 109
Stenzinger, A., Alber, M., Allgäuer, M., Jurmeister, P. S., Bockmayr, M. L., Budczies, J., Lennerz, J. K., Eschrich, J., Kazdal, D. N., Schirmacher, P., et al. (2021) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2021.02.011 · Cited by 94
Saxena, S., & Gyanchandani, M. (2019) · Journal of medical imaging and radiation sciences · doi:10.1016/j.jmir.2019.11.001 · Cited by 93
Gupta, A., Duggal, R., Gehlot, S., Gupta, R., Mangal, A., Kumar, L., Thakkar, N., & Satpathy, D. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101788 · Cited by 89
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Seoni, S., Shahini, A., Meiburger, K. M., Marzola, F., Rotunno, G., Acharya, U. R., Molinari, F., & Salvi, M. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108200 · Cited by 95
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Su, J., Yu, X., Wang, X., Wang, Z., & Chao, G. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107602 · Cited by 88
Homeyer, A., Geißler, C., Schwen, L. O., Zakrzewski, F., Evans, T., Strohmenger, K., Westphal, M., Bülow, R. D., Kargl, M., Karjauv, A., et al. (2022) · Modern Pathology · doi:10.1038/s41379-022-01147-y · Cited by 88
Otálora, S., Atzori, M., Andrearczyk, V., Khan, A., & Müller, H. (2019) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00198 · Cited by 82
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Machine learning methods for histopathological image analysis: updates in 2024
Komura, D., Ochi, M., & Ishikawa, S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.033 · Cited by 76
TSFD-Net: Tissue specific feature distillation network for nuclei segmentation and classification
Ilyas, T., Mannan, Z. I., Khan, A., Azam, S., Kim, H., & Boer, F. D. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.02.020 · Cited by 78
Deep learning generates synthetic cancer histology for explainability and education
Dolezal, J. M., Wolk, R., Hieromnimon, H. M., Howard, F. M., Srisuwananukorn, A., Karpeyev, D., Ramesh, S., Kochanny, S. E., Kwon, J. W., Agni, M., et al. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00399-4 · Cited by 75
Shang, J., Xu, D., Qiu, H., Gao, L., Jiang, C., & Yi, P. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.02.011 · Cited by 75
Altini, N., Cascarano, G. D., Brunetti, A., Marino, F., Rocchetti, M. T., Matino, S., Venere, U., Rossini, M., Pesce, F., Gesualdo, L., et al. (2020) · Electronics · doi:10.3390/electronics9030503 · Cited by 75
Miller, J., Taori, R., Raghunathan, A., Sagawa, S., Koh, P. W., Shankar, V., Liang, P., Carmon, Y., & Schmidt, L. (2021) · arXiv · doi:10.48550/arxiv.2107.04649 · Cited by 74
Integrating pathomics with radiomics and genomics for cancer prognosis: A brief review
Lu, C. Y., Shiradkar, R., & Liu, Z. (2021) · Chinese Journal of Cancer Research · doi:10.21147/j.issn.1000-9604.2021.05.03 · Cited by 71
Tharwat, M., Sakr, N. A., El–Sappagh, S., Soliman, H., Kwak, K. S., & Elmogy, M. M. (2022) · Sensors · doi:10.3390/s22239250 · Cited by 74
Similarity and quality metrics for MR image-to-image translation
Dohmen, M., Klemens, M., Baltruschat, I. M., Truong, T., & Lenga, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-87358-0 · Cited by 74
Exploring linearity of deep neural network trained QSM: QSMnet+
Jung, W., Yoon, J., Ji, S., Choi, J. Y., Kim, J. M., Nam, Y., Kim, E. Y., & Lee, J. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116619 · Cited by 71
Świderska-Chadaj, Ż., de Bel, T., Blanchet, L., Baidoshvili, A., Vossen, D. L. J., van der Laak, J. A. W. M., & Litjens, G. J. S. (2020) · Scientific Reports · doi:10.1038/s41598-020-71420-0 · Cited by 73
Self-citationYamashita, R., Long, J., Banda, S., Shen, J., & Rubin, D. L. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3101985 · Cited by 70
The Digital Brain Tumour Atlas, an open histopathology resource
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Marini, N., Marchesin, S., Otálora, S., Wodziński, M., Caputo, A., van Rijthoven, M., Aswolinskiy, W., Bokhorst, J., Podareanu, D., Petters, E., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00635-4 · Cited by 71
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fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network
Aarthi, C., Dayanand Lal, N., Divakarachari, P. B., & Loganathan, U. (2023) · Brain Sciences · doi:10.3390/brainsci13060893 · Cited by 62
Dunn, C. M., Brettle, D. S., Cockroft, M., Keating, E., Revie, C., & Treanor, D. E. (2024) · Diagnostic Pathology · doi:10.1186/s13000-024-01461-w · Cited by 62
Bándi, P., Balkenhol, M. C. A., van Ginneken, B., van der Laak, J. A. W. M., & Litjens, G. J. S. (2019) · PeerJ · doi:10.7717/peerj.8242 · Cited by 68
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Salvi, M., Mogetta, A., Gambella, A., Molinaro, L., Barreca, A., Papotti, M. G., & Molinari, F. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101930 · Cited by 60
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Shen, Y., Luo, Y., Shen, D. G., & Ke, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16434-7_21 · Cited by 58
Thagaard, J., Broeckx, G., Page, D. B., Jahangir, C. A., Verbandt, S., Kos, Z., Gupta, R., Khiroya, R., AbdulJabbar, K., Haab, G. A., et al. (2023) · The Journal of Pathology · doi:10.1002/path.6155 · Cited by 58
Self-citationWong, A. N. N., He, Z., Leung, K. L., To, C. C. K., Wong, C. Y., Wong, S. C. C., Yoo, J. S., Chan, C. K. R., Chan, A. Z., Lacambra, M. D., et al. (2022) · Cancers · doi:10.3390/cancers14153780 · Cited by 64
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Kim, H., Yoon, H., Thakur, N., Hwang, G., Lee, E., Kim, C., & Chong, Y. (2021) · Scientific Reports · doi:10.1038/s41598-021-01905-z · Cited by 55
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Wahab, N., Miligy, I. M., Dodd, K., Sahota, H., Toss, M. S., Lu, W., Jahanifar, M., Bilal, M., Graham, S., Park, Y., et al. (2022) · The Journal of Pathology Clinical Research · doi:10.1002/cjp2.256 · Cited by 56
Wagner, S. J., Khalili, N., Sharma, R., Boxberg, M., Marr, C., de Back, W., & Peng, T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87237-3_25 · Cited by 52
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Khan, A., Brouwer, N. P. M., Blank, A., Müller, F. C., Soldini, D., Noske, A., Gaus, E., Brandt, S., Nagtegaal, I. D., Dawson, H., et al. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100118 · Cited by 49
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Self-citationShen, Y., Sowmya, A., Luo, Y., Liang, X., Shen, D. G., & Ke, J. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3221724 · Cited by 58
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Zarella, M. D., McClintock, D. S., Batra, H. V., Gullapalli, R. R., Valante, M., Tan, V. O., Dayal, S., Oh, K. S., Lara, H., Garcia, C., et al. (2023) · Journal of medical imaging · doi:10.1117/1.jmi.10.5.051802 · Cited by 49
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A Closer Look at Domain Shift for Deep Learning in HistopathologyPreprint
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Mehrvar, S., Himmel, L. E., Babburi, P., Goldberg, A. L., Guffroy, M., Janardhan, K. S., Krempley, A. L., & Bawa, B. (2021) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_36_21 · Cited by 46
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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