A method for normalizing histology slides for quantitative analysis
Abstract
Inconsistencies in the preparation of histology slides make it difficult to perform quantitative analysis on their results. In this paper we provide two mechanisms for overcoming many of the known inconsistencies in the staining process, thereby bringing slides that were processed or stored under very different conditions into a common, normalized space to enable improved quantitative analysis.
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Specialized methods for stain variation typically fall into one of two categories: data augmentation methods that perturb colors in training images or stain normalization methods that standardize colors across training images.
“These typically fall into one of two categories: data augmentation methods that perturb the colors in the training images (e.g., Liu et al. (2017); Bug et al. (2017); Tellez et al. (2018)) or stain normalization methods that seek to standardize colors across training images (e.g., Macenko et al. (2009); BenTaieb and Hamarneh (2017)).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Macenko et al. (2009)
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A RAPID AND PRECISE METHOD FOR THE DETERMINATION OF UREA
Fawcett, J. K., & Scott, J. E. (1960) · Journal of Clinical Pathology · doi:10.1136/jcp.13.2.156 · Cited by 2,807 · see citing works
Quantification of histochemical staining by color deconvolution.
Ruifrok, A. C. C., & Johnston, D. A. (2001) · PubMed · Cited by 2,029 · see citing works
Improving Melanoma Classification by Integrating Genetic and Morphologic Features
Virós, A., Fridlyand, J., Bauer, J. M., Lasithiotakis, K., Garbe, C., Pinkel, D., & Bastian, B. C. (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050120 · Cited by 376 · see citing works
Distance-Weighted Discrimination
Marron, J. S., Todd, M. J., & Ahn, J. (2007) · Journal of the American Statistical Association · doi:10.1198/016214507000001120 · Cited by 332 · see citing works
Self-citationRuifrok, A. C. C., Katz, R. L., & Johnston, D. A. (2003) · Applied immunohistochemistry & molecular morphology · doi:10.1097/00129039-200303000-00014 · Cited by 165 · see citing works
Unsupervised Color Decomposition of Histologically Stained Tissue Samples
Rabinovich, A., Laris, C. A., Qdm Inc, Agarwal, S., Price, J. H., & Belongie, S. (2003) · Cited by 118 · see citing works
A Framework for the Automated Analysis of Subcellular Patterns in Human Protein Atlas Images
Newberg, J. Y., & Murphy, R. F. (2008) · Journal of Proteome Research · doi:10.1021/pr7007626 · Cited by 105 · see citing works
Factors to keep in mind when introducing virtual microscopy
Glatz‐Krieger, K., Spornitz, U. M., Spatz, A., Mihatsch, M. J., & Glatz, D. (2005) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-005-0112-2 · Cited by 73 · see citing works
Spectral Imaging and Pathology: Seeing More
Levenson, R. M. (2004) · Laboratory Medicine · doi:10.1309/krnf-wqqe-uplq-l76l · Cited by 37 · see citing works
Ljungberg, A., & Johansson, O. (1993) · The Histochemical Journal · doi:10.1007/bf00211769 · Cited by 37 · see citing works
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Chen, R. J., Ding, T., Lu, M. Y., Williamson, D. F. K., Jaume, G., Song, A. H., Chen, B., Zhang, A., Shao, D., Shaban, M., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02857-3 · Cited by 1,560 · see citing works
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
(2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67558-9 · Cited by 1,149 · see citing works
Kather, J. N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis, C.-A., Gaiser, T., Marx, A., Valous, N. A., Ferber, D., et al. (2019) · PLOS Medicine · doi:10.1371/journal.pmed.1002730 · Cited by 1,082 · see citing works
Classification of breast cancer histology images using Convolutional Neural Networks
Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., & Campilho, A. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177544 · Cited by 1,017 · see citing works
Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images
Vahadane, A., Peng, T., Sethi, A., Albarqouni, S., Wang, L., Baust, M., Steiger, K., Schlitter, A. M., Esposito, I., & Navab, N. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2529665 · Cited by 833 · see citing works
Khan, S., Islam, N., Jan, Z., Din, I. U., & Rodrigues, J. J. P. C. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.03.022 · Cited by 796 · see citing works
Deep 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 · see citing works
BACH: Grand challenge on breast cancer histology images
Aresta, G., Araújo, T., Kwok, S., Chennamsetty, S. S., Safwan, M., Alex, V., Marami, B., Prastawa, M. W., Chan, M. S. M., Donovan, M. J., et al. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.05.010 · Cited by 717 · see citing works
Breast Cancer Histopathology Image Analysis: A Review
Veta, M., Pluim, J. P. W., van Diest, P. J., & Viergever, M. A. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2303852 · Cited by 696 · see citing works
Pan-cancer image-based detection of clinically actionable genetic alterations
Kather, J. N., Heij, L. R., Grabsch, H. I., Loeffler, C. M. L., Echle, A., Muti, H. S., Krause, J., Niehues, J., Sommer, K. A. J., Bankhead, P., et al. (2020) · Nature Cancer · doi:10.1038/s43018-020-0087-6 · Cited by 660 · see citing works
Harnessing multimodal data integration to advance precision oncology
Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J., & Shah, S. P. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-021-00408-3 · Cited by 662 · see citing works
Irshad, H., Veillard, A., Roux, L., & Racoceanu, D. (2014) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2013.2295804 · Cited by 641 · see citing works
Tellez, D., Litjens, G., Bándi, P., Bulten, W., Bokhorst, J.-M., Ciompi, F., & van der Laak, J. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101544 · Cited by 649 · see citing works
AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images
Albarqouni, S., Baur, C., Achilles, F., Belagiannis, V., Demirci, S., & Navab, N. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528120 · Cited by 606 · see citing works
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 559 · see citing works
Khan, A. M., Rajpoot, N., Treanor, D. E., & Magee, D. R. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2303294 · Cited by 509 · see citing works
Aeffner, F., Zarella, M. D., Buchbinder, N., Bui, M. M., Goodman, M. R., Hartman, D. J., Lujan, G. M., Molani, M. A., Parwani, A. V., Lillard, K., et al. (2019) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_82_18 · Cited by 408 · see citing works
Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning
Echle, A., Grabsch, H. I., Quirke, P., van den Brandt, P. A., West, N. P., Hutchins, G. G. A., Heij, L. R., Tan, X., Richman, S. D., Krause, J., et al. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.06.021 · Cited by 376 · see citing works
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 · see citing works
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 399 · see citing works
Staingan: Stain Style Transfer for Digital Histological Images
Shaban, M., Baur, C., Navab, N., & Albarqouni, S. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759152 · Cited by 361 · see citing works
Breast cancer histopathological image classification using a hybrid deep neural network
Yan, R., Ren, F., Wang, Z., Wang, L., Zhang, T., Liu, Y., Rao, X., Zheng, C.-H., & Zhang, F. (2019) · Methods · doi:10.1016/j.ymeth.2019.06.014 · Cited by 352 · see citing works
Deep learning-enabled virtual histological staining of biological samples
Bai, B., Yang, X., Li, Y., Zhang, Y., Pillar, N., & Özcan, A. (2023) · Light Science & Applications · doi:10.1038/s41377-023-01104-7 · Cited by 352 · see citing works
Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images
Mahmood, F., Borders, D., Chen, R. J., McKay, G. N., Salimian, K. J., Baras, A. S., & Durr, N. J. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2927182 · Cited by 347 · see citing works
Murtaza, G., Shuib, L., Abdul Wahab, A. W., Mujtaba, G., Mujtaba, G., Nweke, H. F., Al-garadi, M. A., Zulfiqar, F., Raza, G., & Azmi, N. A. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09716-5 · Cited by 342 · see citing works
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 · see citing works
Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge
Veta, M., Heng, Y. J., Stathonikos, N., Bejnordi, B. E., de Beça, F. F., Wollmann, T., Rohr, K., Shah, M., Wang, D., Rousson, M., et al. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.02.012 · Cited by 309 · see citing works
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
Rakhlin, A., Shvets, A. A., Iglovikov, V., & Kalinin, A. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93000-8_83 · Cited by 314 · see citing works
Stain Specific Standardization of Whole-Slide Histopathological Images
Bejnordi, B. E., Litjens, G. J. S., Timofeeva, N., Otte‐Höller, I., Homeyer, A., Karssemeijer, N., & van der Laak, J. A. W. M. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2476509 · Cited by 291 · see citing works
Dietary fructose improves intestinal cell survival and nutrient absorption
Taylor, S. R., Ramsamooj, S., Liang, R. J., Katti, A., Pozovskiy, R., Vasan, N., Hwang, S.-K., Nahiyaan, N., Francoeur, N. J., Schatoff, E. M., et al. (2021) · Nature · doi:10.1038/s41586-021-03827-2 · Cited by 288 · see citing works
Tellez, D., Balkenhol, M., Otte-Höller, I., van de Loo, R., Vogels, R., Bult, P., Wauters, C., Vreuls, W., Mol, S., Karssemeijer, N., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2820199 · Cited by 288 · see citing works
RIC-Unet: An Improved Neural Network Based on Unet for Nuclei Segmentation in Histology Images
Zeng, Z., Xie, W., Zhang, Y., & Lu, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2896920 · Cited by 280 · see citing works
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 · see citing works
“…categories: data augmentation methods that perturb the colors in the training images (e.g., Liu et al. (2017); Bug et al. (2017); Tellez et al. (2018)) or stain normalization methods that seek to standardize colors across training images (e.g., Macenko et al. (2009); BenTaieb and Hamarneh (2017)).”
Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models
Hameed, Z., Zahia, S., Garcia-Zapirain, B., Javier Aguirre, J., & María Vanegas, A. (2020) · Sensors · doi:10.3390/s20164373 · Cited by 277 · see citing works
Computational pathology: Challenges and promises for tissue analysis
Fuchs, T. J., & Buhmann, J. M. (2011) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2011.02.006 · Cited by 267 · see citing works
Pathology imaging informatics for quantitative analysis of whole-slide images
Kothari, S., Phan, J. H., Stokes, T. H., & Wang, M. D. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001540 · Cited by 259 · see citing works
A Stochastic Polygons Model for Glandular Structures in Colon Histology Images
Sirinukunwattana, K., Snead, D. R. J., & Rajpoot, N. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2433900 · Cited by 257 · see citing works
Deep learning for colon cancer histopathological images analysis
Hamida, A. B., Devanne, M., Weber, J., Truntzer, C., Dérangère, V., Ghiringhelli, F., Forestier, G., & Wemmert, C. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104730 · Cited by 253 · see citing works
Hashimoto, N., Fukushima, D., Koga, R., Takagi, Y., Ko, K., Kohno, K., Nakaguro, M., Nakamura, S., Hontani, H., & Takeuchi, I. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00391 · Cited by 251 · see citing works
Kumar, A., Singh, S. K., Saxena, S., Lakshmanan, K. S., Sangaiah, A. K., Chauhan, H., Shrivastava, S., & Singh, R. K. (2019) · Information Sciences · doi:10.1016/j.ins.2019.08.072 · Cited by 255 · see citing works
Swarm learning for decentralized artificial intelligence in cancer histopathology
Saldanha, O. L., Quirke, P., West, N. P., James, J. A., Loughrey, M. B., Grabsch, H. I., Salto-Tellez, M., Alwers, E., Cifci, D., Ghaffari Laleh, N., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01768-5 · Cited by 227 · see citing works
Adversarial Stain Transfer for Histopathology Image Analysis
BenTaieb, A., & Hamarneh, G. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2781228 · Cited by 231 · see citing works
CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation
Zhou, Y., Onder, O. F., Dou, Q., Tsougenis, E., Chen, H., & Heng, P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20351-1_53 · Cited by 229 · see citing works
Chen, C., Chen, C., Yu, W.-H., Chen, S. S., Chang, Y., Hsu, T., Hsiao, M., Yeh, C., & Chen, C. (2021) · Nature Communications · doi:10.1038/s41467-021-21467-y · Cited by 221 · see citing works
Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology
Janowczyk, A. R., Basavanhally, A. N., & Madabhushi, A. (2016) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2016.05.003 · Cited by 211 · see citing works
Ogier du Terrail, J., Leopold, A., Joly, C., Béguier, C., Andreux, M., Maussion, C., Schmauch, B., Tramel, E. W., Bendjebbar, E., Zaslavskiy, M., et al. (2023) · Nature Medicine · doi:10.1038/s41591-022-02155-w · Cited by 217 · see citing works
A survey of methods for 3D histology reconstruction
Pichat, J., Iglesias, J. E., Yousry, T., Ourselin, S., & Modat, M. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.02.004 · Cited by 207 · see citing works
Patch-based system for Classification of Breast Histology images using deep learning
Roy, K., Banik, D., Bhattacharjee, D., & Nasipuri, M. (2018) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2018.11.003 · Cited by 206 · see citing works
Ciompi, F., Geessink, O. G. F., Bejnordi, B. E., de Souza, G. S., Baidoshvili, A., Litjens, G. J. S., van Ginneken, B., Nagtegaal, I. D., & van der Laak, J. A. W. M. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950492 · Cited by 202 · see citing works
Super-resolved spatial transcriptomics by deep data fusion
Bergenstråhle, L., He, B., Bergenstråhle, J., Abalo, X., Mirzazadeh, R., Thrane, K., Ji, A. L., Andersson, A., Larsson, L., Stakenborg, N., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01075-3 · Cited by 188 · see citing works
Classification of breast cancer histology images using incremental boosting convolution networks
Vo, D. M., Nguyen, N.-Q., & Lee, S. (2019) · Information Sciences · doi:10.1016/j.ins.2018.12.089 · Cited by 195 · see citing works
Matek, C., Krappe, S., Münzenmayer, C., Haferlach, T., & Marr, C. (2021) · Blood · doi:10.1182/blood.2020010568 · Cited by 193 · see citing works
BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images
Brancati, N., Anniciello, A. M., Pati, P., Riccio, D., Scognamiglio, G., Jaume, G., Pietro, G. D., Bonito, M. D., Foncubierta–Rodríguez, A., Botti, G., et al. (2022) · Database · doi:10.1093/database/baac093 · Cited by 189 · see citing works
Bejnordi, B. E., Zuidhof, G., Balkenhol, M. C. A., Hermsen, M., Bult, P., van Ginneken, B., Karssemeijer, N., Litjens, G. J. S., & van der Laak, J. A. W. M. (2017) · Journal of medical imaging · doi:10.1117/1.jmi.4.4.044504 · Cited by 186 · see citing works
A study about color normalization methods for histopathology images
Roy, S., Jain, A. K., Lal, S., & Kini, J. R. (2018) · Micron · doi:10.1016/j.micron.2018.07.005 · Cited by 185 · see citing works
High-accuracy prostate cancer pathology using deep learning
Tolkach, Y., Dohmgörgen, T., Toma, M. I., & Kristiansen, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0200-7 · Cited by 183 · see citing works
Laleh, N. G., Muti, H. S., Loeffler, C. M. L., Echle, A., Saldanha, O. L., Mahmood, F., Lu, M. Y., Trautwein, C., Langer, R., Dislich, B., et al. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102474 · Cited by 186 · see citing works
Cellular community detection for tissue phenotyping in colorectal cancer histology images
Javed, S., Mahmood, A., Fraz, M. M., Koohbanani, N. A., Benes, K., Tsang, Y., Hewitt, K. J., Epstein, D., Snead, D. R. J., & Rajpoot, N. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101696 · Cited by 171 · see citing works
Hierarchical graph representations in digital pathology
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 168 · see citing works
A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images
Cui, Y., Zhang, G., Liu, Z., Xiong, Z., & Hu, J. (2019) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-019-02008-8 · Cited by 163 · see citing works
Domain-Adversarial Neural Networks to Address the Appearance Variability of Histopathology Images
Lafarge, M. W., Pluim, J. P. W., Eppenhof, K. A. J., Moeskops, P., & Veta, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67558-9_10 · Cited by 161 · see citing works
Li, Y., Wu, J., & Wu, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2898044 · Cited by 162 · see citing works
Lung and colon cancer classification using medical imaging: a feature engineering approach
Chehade, A. H., Abdallah, N., Marion, J.-M., Oueidat, M., & Chauvet, P. E. (2022) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-022-01139-x · Cited by 160 · see citing works
Mitosis domain generalization in histopathology images — The MIDOG challenge
Aubreville, M., Stathonikos, N., Bertram, C., Klopfleisch, R., ter Hoeve, N. D., Ciompi, F., Wilm, F., Marzahl, C., Donovan, T., Maier, A., et al. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102699 · Cited by 164 · see citing works
Hoang, D.-T., Dinstag, G., Shulman, E. D., Hermida, L. C., Ben-Zvi, D. S., Elis, E., Caley, K., Sammut, S.-J., Sinha, S., Sinha, N., et al. (2024) · Nature Cancer · doi:10.1038/s43018-024-00793-2 · Cited by 157 · see citing works
Wahab, N., Khan, A., & Lee, Y. S. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.04.012 · Cited by 155 · see citing works
Glomerulosclerosis identification in whole slide images using semantic segmentation
Bueno, G., Fernández-Carrobles, M. M., Lucía, G.-L., & Déniz, Ó. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105273 · Cited by 156 · see citing works
An investigation of XGBoost-based algorithm for breast cancer classification
Liew, X. Y., Hameed, N., & Clos, J. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100154 · Cited by 159 · see citing works
Classification of Breast Cancer Histology Images Using Transfer Learning
Vesal, S., Ravikumar, N., Davari, A., Ellmann, S., & Maier, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93000-8_92 · Cited by 153 · see citing works
Classification of Breast Cancer Histology Using Deep Learning
Golatkar, A., Anand, D., & Sethi, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93000-8_95 · Cited by 151 · see citing works
Gadermayr, M., & Tschuchnig, M. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102337 · Cited by 161 · see citing works
Stain normalization of histopathology images using generative adversarial networks
Zanjani, F. G., Zinger, S., Bejnordi, B. E., van der Laak, J. A. W. M., & de With, P. H. N. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363641 · Cited by 148 · see citing works
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 · see citing works
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 · see citing works
Hoque, M. Z., Keskinarkaus, A., Nyberg, P., & Seppänen, T. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101997 · Cited by 153 · see citing works
Toward Explainable Artificial Intelligence for Precision Pathology
Klauschen, F., Dippel, J., Keyl, P., Jurmeister, P. S., Bockmayr, M. L., Möck, A., Buchstab, O., Alber, M., Ruff, L., Montavon, G., et al. (2023) · Annual Review of Pathology Mechanisms of Disease · doi:10.1146/annurev-pathmechdis-051222-113147 · Cited by 144 · see citing works
Zhu, H., Lin, M., Xu, Z., Yao, Z., Chen, H., Alhudhaif, A., & Alenezi, F. S. (2022) · Information Sciences · doi:10.1016/j.ins.2022.06.091 · Cited by 142 · see citing works
Li, X., & Plataniotis, K. N. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2405791 · Cited by 137 · see citing works
A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer
Amgad, M., Hodge, J. M., Elsebaie, M. A. T., Bodelon, C., Puvanesarajah, S., Gutman, D. A., Siziopikou, K. P., Goldstein, J. A., Gaudet, M. M., Teras, L. R., et al. (2024) · Nature Medicine · doi:10.1038/s41591-023-02643-7 · Cited by 137 · see citing works
Chen, H., Li, C., Li, X., Rahaman, M. M., Hu, W., Li, Y., Liu, W., Sun, C., Sun, H., Huang, X., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105265 · Cited by 136 · see citing works
Amgad, M. T., Atteya, L. A., Hussein, H., Mohammed, K. H., Hafiz, E. O. A., Elsebaie, M. A., Alhusseiny, A. M., AlMoslemany, M. A., Elmatboly, A. M., Pappalardo, P. A., et al. (2022) · GigaScience · doi:10.1093/gigascience/giac037 · Cited by 136 · see citing works
Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients
Tsai, P.-C., Lee, T.-H., Kuo, K.-C., Su, F.-Y., Lee, T.-L. M., Marostica, E., Ugai, T., Zhao, M., Lau, M. C., Väyrynen, J. P., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37179-4 · Cited by 136 · see citing works
Automated Histology Analysis: Opportunities for signal processing
McCann, M. T., Ozolek, J. A., Castro, C. A., Parvin, B., & Kovačević, J. (2014) · IEEE Signal Processing Magazine · doi:10.1109/msp.2014.2346443 · Cited by 128 · see citing works
Shahidi, F., Daud, S. M., Abas, H., Ahmad, N. A., & Maarop, N. (2020) · IEEE Access · doi:10.1109/access.2020.3029881 · Cited by 133 · see citing works
A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer
Ho, C., Zhao, Z., Chen, X. F., Sauer, J., Saraf, S. A., Jialdasani, R., Taghipour, K., Sathe, A. R., Khor, L.-Y., Lim, K., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-06264-x · Cited by 128 · see citing works
TIAToolbox as an end-to-end library for advanced tissue image analytics
Pocock, J., Graham, S., Vu, Q. D., Jahanifar, M., Deshpande, S., Hadjigeorghiou, G., Shephard, A., Bashir, R. M. S., Bilal, M., Lu, W., et al. (2022) · Communications Medicine · doi:10.1038/s43856-022-00186-5 · Cited by 128 · see citing works
Veta, M., van Diest, P. J., Jiwa, M., Al-Janabi, S., & Pluim, J. P. W. (2016) · PLOS ONE · doi:10.1371/journal.pone.0161286 · Cited by 127 · see citing works
Muti, H. S., Heij, L. R., Keller, G., Kohlruss, M., Langer, R., Dislich, B., Cheong, J.-H., Kim, Y.-W., Kim, H., Kook, M.-C., et al. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00133-3 · Cited by 126 · see citing works
Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification
Nazeri, K., Aminpour, A., & Ebrahimi, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93000-8_81 · Cited by 126 · see citing works
Le, H., Gupta, R., Hou, L., Abousamra, S., Fassler, D., Torre-Healy, L. A., Moffitt, R. A., Kurç, T., Samaras, D., Batiste, R. C., et al. (2020) · American Journal Of Pathology · doi:10.1016/j.ajpath.2020.03.012 · Cited by 125 · see citing works
Colorectal Histology Tumor Detection Using Ensemble Deep Neural Network
Ghosh, S., Bandyopadhyay, A., Sahay, S., Ghosh, R., Kundu, I., & Santosh, K. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104202 · Cited by 127 · see citing works
Demographic bias in misdiagnosis by computational pathology models
Vaidya, A. J., Chen, R. J., Williamson, D. F. K., Song, A. H., Jaume, G., Yang, Y., Hartvigsen, T., Dyer, E., Lu, M. Y., Lipková, J., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02885-z · Cited by 124 · see citing works
Color standardization in whole slide imaging using a color calibration slide
Bautista, P. A., Hashimoto, N., & Yagi, Y. (2014) · Journal of Pathology Informatics · doi:10.4103/2153-3539.126153 · Cited by 124 · see citing works
Tosta, T. A. A., de Faria, P. R., Neves, L. A., & do Nascimento, M. Z. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.10.004 · Cited by 118 · see citing works
Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification
Vang, Y. S., Chen, Z., & Xie, X. S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93000-8_104 · Cited by 121 · see citing works
Abbasniya, M. R., Sheikholeslamzadeh, S. A., Nasiri, H., & Emami, S. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108382 · Cited by 117 · see citing works
Automated microscopic image analysis for leukocytes identification: A survey
Saraswat, M., & Arya, K. V. (2014) · Micron · doi:10.1016/j.micron.2014.04.001 · Cited by 117 · see citing works
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 117 · see citing works
A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images
Sohail, A., Khan, A., Wahab, N., Zameer, A., & Khan, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-85652-1 · Cited by 117 · see citing works
Adversarial attacks and adversarial robustness in computational pathology
Laleh, N. G., Truhn, D., Veldhuizen, G. P., Han, T., van Treeck, M., Buelow, R. D., Langer, R., Dislich, B., Boor, P., Schulz, V., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33266-0 · Cited by 115 · see citing works
- Authors
- Macenko, M., Niethammer, M., Marron, J. S., Borland, D., Woosley, J. T., Guan, X., Schmitt, C., & Thomas, N. E.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- ORCID
- Venue
- 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
- Publisher
- IEEE
- Published
- 2009-06
- Pages
- 1107-1110
- OpenAlex
- W2132162500
- Cited by
- 1,482
- References
- 10
- Citations by year
- 2026: 170 · 2025: 198 · 2024: 208 · 2023: 182 · 2022: 190 · 2021: 149 · 2020: 114 · 2019: 91 · 2018: 57 · 2017: 41 · 2016: 28 · 2015: 21 · 2014: 14 · 2013: 6 · 2012: 5
- FWCI
- 2.81
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Symposium on Biomedical Imaging (ISBI) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Keywords
- quantitative histology, image normalization, histological image analysis
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