Cited by 1,482AI in cancer detectionCell Image Analysis TechniquesDigital Imaging for Blood Diseases

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

A method for normalizing histology slides for quantitative analysis

Macenko, Marc, Niethammer, Marc, Marron, J. S., Borland, David, Woosley, John T., Xiaojun Guan, Schmitt, Charles, Thomas, Nancy E.·2009·2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

DOI 10.1109/isbi.2009.5193250

Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1109/isbi.2009.5193250

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

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
Quantification of histochemical staining by color deconvolution.
Ruifrok, A. C. C., & Johnston, D. A. (2001) · PubMed · Cited by 2,029
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 383
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
Self-citation
Comparison of Quantification of Histochemical Staining By Hue-Saturation-Intensity (HSI) Transformation and Color-Deconvolution
Ruifrok, A. C. C., Katz, R. L., & Johnston, D. A. (2003) · Applied immunohistochemistry & molecular morphology · doi:10.1097/00129039-200303000-00014 · Cited by 165
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
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
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
Spectral Imaging and Pathology: Seeing More
Levenson, R. M. (2004) · Laboratory Medicine · doi:10.1309/krnf-wqqe-uplq-l76l · Cited by 37
Methodological aspects on immunohistochemistry in dermatology with special reference to neuronal markers
Ljungberg, A., & Johansson, O. (1993) · The Histochemical Journal · doi:10.1007/bf00211769 · Cited by 37

Show all 10 references

Research works printed but not linked: 1

10.1309/KRNFWQQEUPLQL76L · DOI

Sorted by citations

Items 101–200 of 1,001

Region-Based Convolutional Neural Nets for Localization of Glomeruli in Trichrome-Stained Whole Kidney Sections
Bukowy, J. D., Dayton, A., Cloutier, D., Manis, A. D., Staruschenko, A., Lombard, J. H., Woods, L. C. S., Beard, D. A., & Cowley, A. W. (2018) · Journal of the American Society of Nephrology · doi:10.1681/asn.2017111210 · Cited by 113
Stain Color Adaptive Normalization (SCAN) algorithm: Separation and standardization of histological stains in digital pathology
Salvi, M., Michielli, N., & Molinari, F. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105506 · Cited by 113
Deep Convolutional Neural Networks for Breast Cancer Histology Image AnalysisPreprint
Rakhlin, A., Shvets, A. A., Iglovikov, V., & Kalinin, A. A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/259911 · Cited by 116
Artificial intelligence for detection of microsatellite instability in colorectal cancer—a multicentric analysis of a pre-screening tool for clinical application
Echle, A., Ghaffari Laleh, N., Quirke, P., Grabsch, H. I., Muti, H. S., Saldanha, O. L., Brockmoeller, S. F., van den Brandt, P. A., Hutchins, G. G. A., Richman, S. D., et al. (2022) · ESMO Open · doi:10.1016/j.esmoop.2022.100400 · Cited by 111
Classification Of Breast Cancer Histology Images Using ALEXNET
Nawaz, W., Ahmed, S., Tahir, A., & Khan, H. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93000-8_99 · Cited by 118
Breast Cancer Diagnosis with Transfer Learning and Global Pooling
Kassani, S. H., Kassani, P. H., Wesolowski, M. J., Schneider, K. A., & Deters, R. (2019) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc46691.2019.8939878 · Cited by 111
Adaptive color deconvolution for histological WSI normalization
Zheng, Y., Jiang, Z., Zhang, H., Xie, F., Shi, J., & Xue, C. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.01.008 · Cited by 108
End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study
Jiang, X., Hoffmeister, M., Brenner, H., Muti, H. S., Yuan, T., Foersch, S., West, N. P., Brobeil, A., Jonnagaddala, J., Hawkins, N., et al. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00208-x · Cited by 108
From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology
Nahhas, O. S. M. E., van Treeck, M., Wölflein, G., Unger, M., Ligero, M., Lenz, T., Wagner, S. J., Hewitt, K. J., Khader, F., Foersch, S., et al. (2024) · Nature Protocols · doi:10.1038/s41596-024-01047-2 · Cited by 121
Unsupervised Learning for Cell-Level Visual Representation in Histopathology Images With Generative Adversarial Networks
Hu, B., Tang, Y., Chang, E., Fan, Y., Lai, M., & Xu, Y. L. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2852639 · Cited by 110
Computer-Aided Histopathological Image Analysis Techniques for Automated Nuclear Atypia Scoring of Breast Cancer: a Review
Das, A., Nair, P. (. ). M. S., & Peter, S. D. (2020) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-019-00295-z · Cited by 107
Regression-based Deep-Learning predicts molecular biomarkers from pathology slides
El Nahhas, O. S. M., Loeffler, C. M. L., Carrero, Z. I., van Treeck, M., Kolbinger, F. R., Hewitt, K. J., Muti, H. S., Graziani, M., Zeng, Q., Calderaro, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45589-1 · Cited by 108
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
Classification of Histopathological Biopsy Images Using Ensemble of Deep Learning NetworksPreprint
Kassani, S. H., Kassani, P. H., Wesolowski, M. J., Schneider, K. A., & Deters, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.11870 · Cited by 106
Efficient nucleus detector in histopathology images
Vink, J. P., van Leeuwen, M. B., van Deurzen, C. H. M., & de Haan, G. (2012) · Journal of Microscopy · doi:10.1111/jmi.12001 · Cited by 106
Pix2Pix-based Stain-to-Stain Translation: A Solution for Robust Stain Normalization in Histopathology Images Analysis
Salehi, P., & Chalechale, A. (2020) · Iranian Conference on Machine Vision and Image Processing · doi:10.1109/mvip49855.2020.9116895 · Cited by 104
Classification of Breast Cancer Histopathological Images Using Discriminative Patches Screened by Generative Adversarial Networks
Man, R., Yang, P., & Xu, B. (2020) · IEEE Access · doi:10.1109/access.2020.3019327 · Cited by 103
Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study
Niehues, J., Quirke, P., West, N. P., Grabsch, H. I., van Treeck, M., Schirris, Y., Veldhuizen, G. P., Hutchins, G. G. A., Richman, S. D., Foersch, S., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.100980 · Cited by 100
SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging
Duggal, R., Gupta, A., Gupta, R., & Mallick, P. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66179-7_50 · Cited by 99
Structure-preserved color normalization for histological images
Vahadane, A., Peng, T., Albarqouni, S., Baust, M., Steiger, K., Schlitter, A. M., Sethi, A., Esposito, I., & Navab, N. (2015) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7164042 · Cited by 99
Diffuse axonal injury has a characteristic multidimensional MRI signature in the human brain
Benjamini, D., Iacono, D., Komlosh, M. E., Perl, D. P., Brody, D. L., & Basser, P. J. (2020) · Brain · doi:10.1093/brain/awaa447 · Cited by 94
Breast histopathological image analysis using image processing techniques for diagnostic purposes: A methodological review
Rashmi, R., Prasad, K., & Udupa, C. B. K. (2021) · Journal of Medical Systems · doi:10.1007/s10916-021-01786-9 · Cited by 105
Generative Adversarial Networks for Facilitating Stain-Independent Supervised and Unsupervised Segmentation: A Study on Kidney Histology
Gadermayr, M., Gupta, L., Appel, V., Boor, P., Klinkhammer, B. M., & Merhof, D. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2899364 · Cited by 98
Persistent Homology for the Quantitative Evaluation of Architectural Features in Prostate Cancer Histology
Lawson, P., Sholl, A. B., Brown, J. Q., Fasy, B. T., & Wenk, C. (2019) · Scientific Reports · doi:10.1038/s41598-018-36798-y · Cited by 95
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
Learning Where to See: A Novel Attention Model for Automated Immunohistochemical Scoring
Qaiser, T., & Rajpoot, N. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2907049 · Cited by 96
Deep learning in prostate cancer diagnosis and Gleason grading in histopathology images: An extensive study
Linkon, A. H. M., Labib, M. M., Hasan, T., Hossain, M. T., & Marium-E-Jannat (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100582 · Cited by 104
The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review
Kanwal, N., Pérez-Bueno, F., Schmidt, A., Engan, K., & Molina, R. (2022) · IEEE Access · doi:10.1109/access.2022.3176091 · Cited by 109
Encrypted federated learning for secure decentralized collaboration in cancer image analysis
Truhn, D., Arasteh, S. T., Saldanha, O. L., Müller‐Franzes, G. A., Khader, F., Quirke, P., West, N. P., Gray, R. G., Hutchins, G. G. A., James, J. A., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103059 · Cited by 111
Automatic batch-invariant color segmentation of histological cancer images
Kothari, S., Phan, J. H., Moffitt, R. A., Stokes, T. H., Hassberger, S. E., Chaudry, Q., Young, A. N., & Wang, M. D. (2011) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2011.5872492 · Cited by 93
Micro-CT for Biological and Biomedical Studies: A Comparison of Imaging Techniques
Keklikoglou, K., Arvanitidis, C., Chatzigeorgiou, G., Chatzinikolaou, E., Karagiannidis, E., Κoletsa, T., Magoulas, A., Makris, K., Mavrothalassitis, G., Papanagnou, E., et al. (2021) · Journal of Imaging · doi:10.3390/jimaging7090172 · Cited by 92
Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images
Farahani, H., Boschman, J., Farnell, D., Darbandsari, A., Zhang, A. A., Ahmadvand, P., Jones, S. J. M., Huntsman, D. D., Köbel, M., Gilks, C. B., et al. (2022) · Modern Pathology · doi:10.1038/s41379-022-01146-z · Cited by 92
Machine Learning Methods for Computer-Aided Breast Cancer Diagnosis Using Histopathology: A Narrative Review
Saxena, S., & Gyanchandani, M. (2019) · Journal of medical imaging and radiation sciences · doi:10.1016/j.jmir.2019.11.001 · Cited by 93
GCTI-SN: Geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images
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
Pixelwise H-score: A novel digital image analysis-based metric to quantify membrane biomarker expression from immunohistochemistry images
Ram, S., Vizcarra, P., Whalen, P. M., Deng, S., Painter, C., Jackson-Fisher, A. J., Pirie-Shepherd, S. R., Xia, X., & Powell, E. L. (2021) · PLoS ONE · doi:10.1371/journal.pone.0245638 · Cited by 93
Multiclass classification of breast cancer histopathology images using multilevel features of deep convolutional neural network
Hameed, Z., García-Zapirain, B., Aguirre, J. J., & Isaza-Ruget, M. A. (2022) · Scientific Reports · doi:10.1038/s41598-022-19278-2 · Cited by 95
Stain Deconvolution Using Statistical Analysis of Multi-Resolution Stain Colour Representation
Alsubaie, N. M., Trahearn, N., Raza, S. E. A., Snead, D. R. J., & Rajpoot, N. (2017) · PLoS ONE · doi:10.1371/journal.pone.0169875 · Cited by 88
All you need is data preparation: A systematic review of image harmonization techniques in Multi-center/device studies for medical support systems
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
Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma
Caldéraro, J., Laleh, N. G., Zeng, Q., Maillé, P., Favre, L., Pujals, A., Klein, C., Bazille, C., Heij, L. R., Uguen, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43749-3 · Cited by 86
Roto-translation equivariant convolutional networks: Application to histopathology image analysis
Lafarge, M. W., Bekkers, E. J., Pluim, J. P. W., Duits, R., & Veta, M. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101849 · Cited by 84
Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
Wang, Y., Kartasalo, K., Weitz, P., Ács, B., Valkonen, M., Larsson, C., Ruusuvuori, P., Hartman, J., & Rantalainen, M. (2021) · Cancer Research · doi:10.1158/0008-5472.can-21-0482 · Cited by 82
Artificial intelligence applications in histopathology
Bahadir, C. D., Omar, M., Rosenthal, J., Marchionni, L., Liechty, B., Pisapia, D. J., & Sabuncu, M. R. (2024) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-023-00012-7 · Cited by 93
Staining Invariant Features for Improving Generalization of Deep Convolutional Neural Networks in Computational Pathology
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
MaskMitosis: a deep learning framework for fully supervised, weakly supervised, and unsupervised mitosis detection in histopathology images
Sebai, M., Wang, X., & Wang, T. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02175-z · Cited by 82
MCUa: Multi-Level Context and Uncertainty Aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification
Senousy, Z., Abdelsamea, M. M., Gaber, M. M., Abdar, M., Acharya, U. R., Khosravi, A., & Nahavandi, S. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3107446 · Cited by 83
Weakly supervised annotation‐free cancer detection and prediction of genotype in routine histopathology
Schrammen, P. L., Laleh, N. G., Echle, A., Truhn, D., Schulz, V., Brinker, T. J., Brenner, H., Chang-Claude, J. C., Alwers, E., Brobeil, A., et al. (2021) · The Journal of Pathology · doi:10.1002/path.5800 · Cited by 81
Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review
Xie, X. S., Wang, X., Liang, Y., Yang, J., Wu, Y. P., Li, L., Sun, X., Bing, P., He, B., Tian, G., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.763527 · Cited by 85
Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning
Wakili, M. A., Shehu, H. A., Sharif, M. H., Sharif, M. H. U., Umar, A., Kusetoğulları, H., İnce, İ. F., & Uyaver, Ş. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/8904768 · Cited by 94
Deep learning detects genetic alterations in cancer histology generated by adversarial networks
Krause, J., Grabsch, H. I., Kloor, M., Jendrusch, M., Echle, A., Buelow, R. D., Boor, P., Luedde, T., Brinker, T. J., Trautwein, C., et al. (2021) · The Journal of Pathology · doi:10.1002/path.5638 · Cited by 82
Cell Nuclei Segmentation in Cytological Images Using Convolutional Neural Network and Seeded Watershed Algorithm
Kowal, M., Żejmo, M., Skobel, M., Korbicz, J., & Monczak, R. (2019) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-019-00200-8 · Cited by 81
Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images
Fassler, D., Abousamra, S., Gupta, R., Chen, C., Zhao, M., Paredes, D., Batool, S. A., Knudsen, B. S., Escobar‐Hoyos, L. F., Shroyer, K. R., et al. (2020) · Diagnostic Pathology · doi:10.1186/s13000-020-01003-0 · Cited by 79
Deep learning-based tumor microenvironment analysis in colon adenocarcinoma histopathological whole-slide images
Jiao, Y., Li, J., Qian, C., & Fei, S. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106047 · Cited by 83
Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer
Brockmoeller, S. F., Echle, A., Laleh, N. G., Eiholm, S., Malmstrøm, M. L., Kühlmann, T. P., Levic, K., Grabsch, H. I., West, N. P., Saldanha, O. L., et al. (2021) · The Journal of Pathology · doi:10.1002/path.5831 · Cited by 78
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
SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells
Tan, X., Su, A., Tran, M., & Nguyen, Q. H. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz914 · Cited by 76
Deep learning identifies morphological features in breast cancer predictive of cancer ERBB2 status and trastuzumab treatment efficacy
Bychkov, D., Linder, N., Tiulpin, A., Kücükel, H., Lundin, M., Nordling, S., Sihto, H. J., Isola, J. J., Lehtimäki, T., Kellokumpu‐Lehtinen, P., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-83102-6 · Cited by 76
Neural Stain-Style Transfer Learning using GAN for Histopathological ImagesPreprint
Cho, H., Lim, S., Choi, G., & Min, H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.08543 · Cited by 106
Deep learning can predict lymph node status directly from histology in colorectal cancer
Kiehl, L., Kuntz, S., Höhn, J., Jutzi, T. B., Krieghoff‐Henning, E., Kather, J. N., Holland‐Letz, T., Kopp‐Schneider, A., Chang-Claude, J. C., Brobeil, A., et al. (2021) · European Journal of Cancer · doi:10.1016/j.ejca.2021.08.039 · Cited by 79
Breast Cancer Classification From Histopathological Images Using Resolution Adaptive Network
Zhou, Y., Zhang, C., & Gao, S. (2022) · IEEE Access · doi:10.1109/access.2022.3163822 · Cited by 81
Evaluating stability of histomorphometric features across scanner and staining variations: prostate cancer diagnosis from whole slide images
Leo, P., Lee, G., Shih, N. N. C., Elliott, R. M., Feldman, M. D., & Madabhushi, A. (2016) · Journal of medical imaging · doi:10.1117/1.jmi.3.4.047502 · Cited by 74
A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology
Paeng, K., Hwang, S., Park, S., & Kim, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67558-9_27 · Cited by 73
Classification of Hematoxylin and Eosin‐Stained Breast Cancer Histology Microscopy Images Using Transfer Learning with EfficientNets
Munien, C., & Viriri, S. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/5580914 · Cited by 74
HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification
Pati, P., Jaume, G., Fernandes, L. A., Foncubierta–Rodríguez, A., Feroce, F., Anniciello, A. M., Scognamiglio, G., Brancati, N., Riccio, D., Bonito, M. D., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60365-6_20 · Cited by 74
Unsupervised Domain Adaptation for Classification of Histopathology Whole-Slide Images
Ren, J., Hacihaliloglu, I., Singer, E. A., Foran, D. J., & Qi, X. (2019) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00102 · Cited by 70
Learning Domain-Agnostic Visual Representation for Computational Pathology Using Medically-Irrelevant Style Transfer Augmentation
Yamashita, 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
Mitotic nuclei analysis in breast cancer histopathology images using deep ensemble classifier
Sohail, A., Khan, A., Nisar, H., Tabassum, S., & Zameer, A. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102121 · Cited by 72
Classification of Breast Cancer Histology Images Using Transfer Learning
Ahmad, H. M., Ghuffar, S., & Khurshid, K. (2019) · International Bhurban Conference on Applied Sciences and Technology (IBCAST) · doi:10.1109/ibcast.2019.8667221 · Cited by 72
SHIFT: speedy histological-to-immunofluorescent translation of a tumor signature enabled by deep learning
Burlingame, E. A., McDonnell, M., Schau, G. F., Thibault, G., Lanciault, C., Morgan, T. K., Johnson, B., Corless, C. L., Gray, J. W., & Chang, Y. H. (2020) · Scientific Reports · doi:10.1038/s41598-020-74500-3 · Cited by 63
Normalization of HE-stained histological images using cycle consistent generative adversarial networks
Runz, M., Rusche, D., Schmidt, S., Weihrauch, M. R., Hesser, J., & Weis, C. (2021) · Diagnostic Pathology · doi:10.1186/s13000-021-01126-y · Cited by 68
The utility of color normalization for AI ‐based diagnosis of hematoxylin and eosin‐stained pathology images
Boschman, J., Farahani, H., Darbandsari, A., Ahmadvand, P., Spankeren, A. V., Farnell, D., Levine, A., Naso, J. R., Churg, A., Jones, S. J. M., et al. (2021) · The Journal of Pathology · doi:10.1002/path.5797 · Cited by 72
Fuzzy ensemble of deep learning models using choquet fuzzy integral, coalition game and information theory for breast cancer histology classification
Bhowal, P., Sen, S., Velásquez, J. D., & Sarkar, R. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116167 · Cited by 68
Learning generalizable AI models for multi-center histopathology image classification
Asadi-Aghbolaghi, M., Darbandsari, A., Zhang, A. W., Contreras‐Sanz, A., Boschman, J., Ahmadvand, P., Köbel, M., Farnell, D., Huntsman, D. D., Churg, A., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00652-4 · Cited by 77
Removing Batch Effects From Histopathological Images for Enhanced Cancer Diagnosis
Kothari, S., Phan, J. H., Stokes, T. H., Osunkoya, A. O., Young, A. N., & Wang, M. D. (2013) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2276766 · Cited by 68
Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach
Deif, M. A., Attar, H. H., Amer, A., Elhaty, I. A., Khosravi, M. R., & Solyman, A. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/6364102 · Cited by 76
Slideflow: deep learning for digital histopathology with real-time whole-slide visualization
Dolezal, J. M., Kochanny, S. E., Dyer, E., Ramesh, S., Srisuwananukorn, A., Sacco, M., Howard, F. M., Li, A., Mohan, P., & Pearson, A. T. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05758-x · Cited by 86
Appearance Normalization of Histology Slides
Niethammer, M., Borland, D., Marron, J. S., Woosley, J. T., & Thomas, N. E. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15948-0_8 · Cited by 67
Self-citation
Breast cancer detection from biopsy images using nucleus guided transfer learning and belief based fusion
George, K., Faziludeen, S., Sankaran, P., & Paul Joseph, K. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103954 · Cited by 66
A generalizable and robust deep learning algorithm for mitosis detection in multicenter breast histopathological images
Wang, X., Zhang, J., Yang, S., Xiang, J., Luo, F., Wang, M., Zhang, J., Yang, W., Huang, J., & Han, X. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102703 · Cited by 66
hist2RNA: An Efficient Deep Learning Architecture to Predict Gene Expression from Breast Cancer Histopathology Images
Mondol, R. K., Millar, E. K. A., Graham, P., Browne, L. H., Sowmya, A., & Meijering, E. H. W. (2023) · Cancers · doi:10.3390/cancers15092569 · Cited by 71
Monofacial vs Bifacial Solar Photovoltaic Systems in Snowy EnvironmentsPreprint
Hayibo, K. S., Petsiuk, A. L., Mayville, P. J., Brown, L. E., & Pearce, J. M. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4056922 · Cited by 66
SDCT-AuxNet : DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis
Gehlot, S., Gupta, A., & Gupta, R. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101661 · Cited by 65
Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: A deep learning approach
Eycke, Y. V., Balsat, C., Verset, L., Debeir, O., Salmon, I. J., & Decaestecker, C. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.07.004 · Cited by 65
Image processing in digital pathology: an opportunity to solve inter-batch variability of immunohistochemical staining
Eycke, Y. V., Allard, J., Salmon, I. J., Debeir, O., & Decaestecker, C. (2017) · Scientific Reports · doi:10.1038/srep42964 · Cited by 64
Computational Histological Staining and Destaining of Prostate Core Biopsy RGB Images with Generative Adversarial Neural Networks
Rana, A., Yauney, G., Lowe, A. C.-Y., & Shah, P. P. (2018) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2018.00133 · Cited by 64
Identification of Colon Cancer Using Multi-Scale Feature Fusion Convolutional Neural Network Based on Shearlet Transform
Liang, M., Ren, Z., Yang, J., Feng, W., & Li, B. (2020) · IEEE Access · doi:10.1109/access.2020.3038764 · Cited by 63
Applications of Topological Data Analysis in Oncology
Bukkuri, A., Andor, N., & Darcy, I. K. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.659037 · Cited by 74
Deep transfer learning based model for colorectal cancer histopathology segmentation: A comparative study of deep pre-trained models
Kassani, S. H., Kassani, P. H., Wesolowski, M. J., Schneider, K. A., & Deters, R. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104669 · Cited by 63
HWDCNN: Multi-class recognition in breast histopathology with Haar wavelet decomposed image based convolution neural network
Kausar, T., Wang, M., Idrees, M., & Lu, Y. (2019) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2019.09.003 · Cited by 63
Deep Learning of Histopathology Images at the Single Cell Level
Lee, K., Lockhart, J. H., Xie, M., Chaudhary, R., Slebos, R. J. C., Flores, E. R., Chung, C. H., & Tan, A. C. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.754641 · Cited by 67
Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review
Tamang, L. D., & Kim, B. W. (2021) · Applied Sciences · doi:10.3390/app112210982 · Cited by 62
Domain Generalization in Computational Pathology: Survey and Guidelines
Jahanifar, M., Raza, M., Xu, K., Vuong, T. T. L., Jewsbury, R., Shephard, A., Tajeddin, N. Z., Kwak, J. T., Raza, S. E. A., Minhas, F., et al. (2025) · ACM Computing Surveys · doi:10.1145/3724391 · Cited by 86
Multiscale Integration of -Omic, Imaging, and Clinical Data in Biomedical Informatics
Phan, J. H., Quo, C. F., Cheng, C., & Wang, M. D. (2012) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2012.2212427 · Cited by 62
FalseColor-Python: A rapid intensity-leveling and digital-staining package for fluorescence-based slide-free digital pathology
Serafin, R., Xie, W., Glaser, A. K., & Liu, J. T. C. (2020) · PLoS ONE · doi:10.1371/journal.pone.0233198 · Cited by 59
Mapping astrogliosis in the individual human brain using multidimensional MRI
Benjamini, D., Priemer, D. S., Perl, D. P., Brody, D. L., & Basser, P. J. (2022) · Brain · doi:10.1093/brain/awac298 · Cited by 60
CervixFormer: A Multi-scale swin transformer-Based cervical pap-Smear WSI classification framework
Khan, A. A., Han, S.-H., Ilyas, N., Lee, Y., & Lee, B. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107718 · Cited by 63
Overcoming the limitations of patch-based learning to detect cancer in whole slide images
Ciga, O., Xu, T., Nofech‐Mozes, S., Noy, S., Lu, F., & Martel, A. L. (2021) · Scientific Reports · doi:10.1038/s41598-021-88494-z · Cited by 59
Pseudo Labeling Methods for Semi-Supervised Semantic Segmentation: A Review and Future Perspectives
Ran, L., Li, Y., Liang, G., & Zhang, Y. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3508768 · Cited by 66
Automated analysis and classification of melanocytic tumor on skin whole slide images
Xu, H., Lu, C. Y., Berendt, R. C., Jha, N., & Mandal, M. K. (2018) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2018.01.008 · Cited by 59
Deep Convolutional Neural Network-Based Lymph Node Metastasis Prediction for Colon Cancer Using Histopathological Images
Kwak, M. S., Lee, H. H., Yang, J. M., Cha, J. M., Jeon, J. W., Yoon, J. Y., & Kim, H. I. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.619803 · Cited by 59
A review: The detection of cancer cells in histopathology based on machine vision
He, W., Liu, T., Han, Y., Ming, W., Du, J., Liu, Y., Yang, Y. H., Wang, L., Jiang, Z., Wang, Y., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105636 · Cited by 58

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: Niethammer, Marc
Marron, J. S.
Borland, David
Woosley, John T.
Xiaojun Guan
Schmitt, Charles
Thomas, Nancy E.
Venue: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
Publisher: IEEE
Published: 2009-06
Pages: 1107-1110
DOI: 10.1109/isbi.2009.5193250
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

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

A method for normalizing histology slides for quantitative analysis

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks