A method for normalizing histology slides for quantitative analysis

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

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

Stain Mix-Up: Unsupervised Domain Generalization for Histopathology Images

Chang, J.-R., Wu, M.-S., Yu, W.-H., Chen, C., Yang, C.-K., Lin, Y., & Yeh, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87199-4_11 · Cited by 36

Artificial Intelligence–Assisted Classification of Gliomas Using Whole Slide Images

Jose, L., Liu, S., Russo, C., Cong, C., Song, Y., Rodriguez, M. L., & Ieva, A. D. (2022) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2021-0518-oa · Cited by 36

Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques

Baghdadi, N. A., Maklad, A. S., Malki, A., & Deif, M. A. (2022) · Sensors · doi:10.3390/s22103846 · Cited by 35

Pathomic Features Reveal Immune and Molecular Evolution From Lung Preneoplasia to Invasive Adenocarcinoma

Chen, P., Rojas, F. R., Hu, X., Serrano, A. G., Zhu, B., Chen, H., Hong, L., Bandyoyadhyay, R., Aminu, M., Kalhor, N., et al. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100326 · Cited by 35

Cervical cell’s nucleus segmentation through an improved UNet architecture

Rasheed, A., Shirazi, S. H., Umar, A. I., Shahzad, M., Yousaf, W., & Khan, Z. (2023) · PLoS ONE · doi:10.1371/journal.pone.0283568 · Cited by 35

Evaluating the effectiveness of stain normalization techniques in automated grading of invasive ductal carcinoma histopathological images

Voon, W., Hum, Y. C., Tee, Y. K., Yap, W., Nisar, H., Mokayed, H., Gupta, N., & Lai, K. W. (2023) · Scientific Reports · doi:10.1038/s41598-023-46619-6 · Cited by 38

A Two-Stage U-Net Algorithm for Segmentation of Nuclei in H&E-Stained Tissues

Mahbod, A., Schaefer, G., Ellinger, I., Ecker, R., Smedby, Ö., & Wang, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-23937-4_9 · Cited by 35

Unsupervised method for normalization of hematoxylin-eosin stain in histological images

Tosta, T. A. A., de Faria, P. R., Servato, J. P. S., Neves, L. A., Roberto, G. F., Martins, A. S., & do Nascimento, M. Z. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.101646 · Cited by 35

Reducing annotation effort in digital pathology: A Co-Representation learning framework for classification tasks

Pati, P., Foncubierta–Rodríguez, A., Göksel, O., & Gabrani, M. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101859 · Cited by 36

Retinex model based stain normalization technique for whole slide image analysis

Hoque, M. Z., Keskinarkaus, A., Nyberg, P., & Seppänen, T. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101901 · Cited by 35

A new complete color normalization method for H&E stained histopatholgical images

Vijh, S., Saraswat, M., & Kumar, S. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02231-7 · Cited by 35

Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology

Boyd, J., Liashuha, M., Deutsch, É., Nikos Paragios, A., Christodoulidis, S., & Vakalopoulou, M. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00077 · Cited by 39

Improving feature extraction from histopathological images through a fine-tuning ImageNet model

Li, X., Cen, M., Xu, J., Zhang, H., & Xu, X. S. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100115 · Cited by 36

Deep learning-based subtyping of gastric cancer histology predicts clinical outcome: a multi-institutional retrospective study

Veldhuizen, G. P., Röcken, C., Behrens, H., Çifçi, D., Muti, H. S., Yoshikawa, T., Arai, T., Oshima, T., Tan, P. B. O., Ebert, M. P. A., et al. (2023) · Gastric Cancer · doi:10.1007/s10120-023-01398-x · Cited by 40

Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality

Zhou, J., pour, A. F., Deirawan, H., Daaboul, F., Aung, T. N., Beydoun, R., Ahmed, F. S., & Chuang, J. H. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104726 · Cited by 35

Detection of nuclei in H&E stained sections using convolutional neural networks

Khoshdeli, M., Cong, R., & Parvin, B. (2017) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2017.7897216 · Cited by 35

Enhanced Cycle-Consistent Generative Adversarial Network for Color Normalization of H&E Stained Images

Zhou, N., Cai, D., Han, X., & Yao, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32239-7_77 · Cited by 34

Breast cancer nuclei segmentation and classification based on a deep learning approach

Kowal, M., Skobel, M., Gramacki, A., & Korbicz, J. (2021) · International Journal of Applied Mathematics and Computer Science · doi:10.34768/amcs-2021-0007 · Cited by 34

Blind color deconvolution, normalization, and classification of histological images using general super Gaussian priors and Bayesian inference

Pérez-Bueno, F., Miguel Vega, B., Sales, M. A., Aneiros‐Fernández, J., Naranjo, V., Molina, R., & Katsaggelos, A. K. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106453 · Cited by 34

HistoCartography: A Toolkit for Graph Analytics in Digital PathologyPreprint

Jaume, G., Pati, P., Anklin, V., Foncubierta–Rodríguez, A., & Gabrani, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.10073 · Cited by 35

NASDM: Nuclei-Aware Semantic Histopathology Image Generation Using Diffusion Models

Shrivastava, A., & Fletcher, P. T. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43987-2_76 · Cited by 33

Automatic identification and delineation of germ layer components in H&E stained images of teratomas derived from human and nonhuman primate embryonic stem cells

Bhagavatula, R., Fickus, M. C., Kelly, W., Guo, C., Ozolek, J. A., Castro, C. A., & Kovacevic, J. (2010) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2010.5490168 · Cited by 33

IHC Color Histograms for Unsupervised Ki67 Proliferation Index Calculation

Geread, R. S., Morreale, P., Dony, R. D., Brouwer, E. R., Wood, G. A., Androutsos, D., & Khademi, A. (2019) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00226 · Cited by 33

Quantitative Whole Slide Assessment of Tumor-Infiltrating CD8-Positive Lymphocytes in ER-Positive Breast Cancer in Relation to Clinical Outcome

Krijgsman, D., van Leeuwen, M. B., van der Ven, J., Almeida, V., Vlutters, R., Halter, D., Kuppen, P. J. K., van de Velde, C. J. �., & Wimberger-Friedl, R. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3003475 · Cited by 35

Decidual Vasculopathy Identification in Whole Slide Images Using Multiresolution Hierarchical Convolutional Neural Networks

Clymer, D. R., Kostadinov, S., Catov, J. M., Skvarca, L. B., Pantanowitz, L., Cagan, J., & LeDuc, P. R. (2020) · American Journal Of Pathology · doi:10.1016/j.ajpath.2020.06.014 · Cited by 33

3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images

Senousy, Z., Abdelsamea, M. M., Mohamed, M. M., & Gaber, M. M. (2021) · Entropy · doi:10.3390/e23050620 · Cited by 34

Breast cancer intelligent analysis of histopathological data: A systematic review

Zeiser, F. A., da Costa, C. A., Roehe, A. V., Righi, R. D. R., & Marques, N. C. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107886 · Cited by 33

Image Analysis in Digital Pathology Utilizing Machine Learning and Deep Neural Networks

Amerikanos, P. P., & Maglogiannis, I. G. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12091444 · Cited by 31

Deep learning in computational dermatopathology of melanoma: A technical systematic literature review

Sauter, D., Lodde, G. C., Nensa, F., Schadendorf, D., Livingstone, E., & Kukuk, M. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107083 · Cited by 37

H&E stain augmentation improves generalization of convolutional networks for histopathological mitosis detection

Balkenhol, M. C. A., Karssemeijer, N., Litjens, G. J. S., van der Laak, J. A. W. M., Ciompi, F., & Tellez, D. (2018) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2293048 · Cited by 34

Deep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer

Barrera, C., Corredor, G., Viswanathan, V. S., Ding, R., Toro, P., Fu, P., Buzzy, C. A., Lu, C. Y., Velu, P., Zens, P., et al. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00403-x · Cited by 34

Built to Last? Reproducibility and Reusability of Deep Learning Algorithms in Computational Pathology

Wagner, S. J., Matek, C., Boushehri, S. S., Boxberg, M., Lamm, L., Sadafi, A., Waibel, D., Marr, C., & Peng, T. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100350 · Cited by 34

An international study of stain variability in histopathology using qualitative and quantitative analysis

Dunn, C. M., Brettle, D. S., Hodgson, C., Hughes, R., & Treanor, D. E. (2025) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2025.100423 · Cited by 40

Maximized Inter-Class Weighted Mean for Fast and Accurate Mitosis Cells Detection in Breast Cancer Histopathology Images

Nateghi, R., Danyali, H., & Helfroush, M. S. (2017) · Journal of Medical Systems · doi:10.1007/s10916-017-0773-9 · Cited by 32

Feature-Enhanced Graph Networks for Genetic Mutational Prediction Using Histopathological Images in Colon Cancer

Ding, K., Liu, Q., Lee, E. H., Zhou, M., Lu, A., & Zhang, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59713-9_29 · Cited by 32

All You Need is Color: Image Based Spatial Gene Expression Prediction Using Neural Stain Learning

Dawood, M., Branson, K., Rajpoot, N., & Minhas, F. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-93733-1_32 · Cited by 30

Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge

Wilm, F., Marzahl, C., Breininger, K., & Aubreville, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-97281-3_1 · Cited by 32

Automatic Detection Metastasis in Breast Histopathological Images Based on Ensemble Learning and Color Adjustment

Luz, D. D. S., Lima, T. J. B., Silva, R., Magalhães, D. M. V., & Araújo, F. H. D. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103564 · Cited by 32

Appearance normalization of histology slides

Vicory, J., Couture, H. D., Thomas, N. E., Borland, D., Marron, J. S., Woosley, J. T., & Niethammer, M. (2015) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2015.03.005 · Cited by 32

Gland segmentation in prostate histopathological images

Singh, M., Kalaw, E., Giron, D. M., Chong, K.-T., Tan, C. L., & Lee, H. K. (2017) · Journal of medical imaging · doi:10.1117/1.jmi.4.2.027501 · Cited by 32

Densely Convolutional Spatial Attention Network for nuclei segmentation of histological images for computational pathology

Sumon, R. I., Bhattacharjee, S., Hwang, Y.-B., Rahman, H., Kim, H., Ryu, W., Kim, D. M., Cho, N.-H., & Choi, H. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1009681 · Cited by 34

Artificial Intelligence–Based Tool for Tumor Detection and Quantitative Tissue Analysis in Colorectal Specimens

Griem, J., Eich, M., Schallenberg, S., Pryalukhin, A. E., Bychkov, A., Fukuoka, J., Zayats, V., Hulla, W., Munkhdelger, J., Seper, A., et al. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100327 · Cited by 35

The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue

Weitz, P., Valkonen, M., Solorzano, L., Carr, C., Kartasalo, K., Boissin, C., Koivukoski, S., Kuusela, A., Rasic, D., Feng, Y., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103257 · Cited by 35

Statistical color texture descriptors for histological images analysis

Hervé, N., Servais, A., Thervet, É., Olivo‐Marín, J., & Meas‐Yedid, V. (2011) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2011.5872508 · Cited by 31

On Edge Computing for Remote Pathology Consultations and Computations

Sacco, A., Esposito, F., Marchetto, G., Kolar, G. R., & Schwetye, K. E. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3007661 · Cited by 31

RestainNet: A self-supervised digital re-stainer for stain normalization

Zhao, B., Han, C., Pan, X., Lin, J., Yi, Z., Liang, C., Chen, X., Li, B., Qiu, W., Li, D., et al. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108304 · Cited by 35

FabNet: A Features Agglomeration-Based Convolutional Neural Network for Multiscale Breast Cancer Histopathology Images Classification

Amin, M., & Ahn, H. (2023) · Cancers · doi:10.3390/cancers15041013 · Cited by 31

PAIP 2020: Microsatellite instability prediction in colorectal cancer

Kim, K. M., Lee, K., Cho, S., Kang, D. U., Park, S., Kang, Y., Kim, H. J., Choe, G., Moon, K. C., Lee, K. S., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102886 · Cited by 30

Enabling large-scale screening of Barrett’s esophagus using weakly supervised deep learning in histopathology

Bouzid, K., Sharma, H., Killcoyne, S., Castro, D. C., Schwaighofer, A., Ilse, M., Salvatelli, V., Oktay, O., Murthy, S., Bordeaux, L., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46174-2 · Cited by 35

Impact of stain variation and color normalization for prognostic predictions in pathology

Lin, S., Zhou, H., Watson, M. A., Govindan, R., Côté, R. J., & Yang, C. (2025) · Scientific Reports · doi:10.1038/s41598-024-83267-w · Cited by 33

Interpretable multi-modal artificial intelligence model for predicting gastric cancer response to neoadjuvant chemotherapy

Gao, P., Xiao, Q., Tan, H. J. J., Song, J., Fu, Y., Xu, J., Zhao, J., Miao, Y., Li, X., Yi, J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101848 · Cited by 30

Tissue Phenomics for prognostic biomarker discovery in low- and intermediate-risk prostate cancer

Harder, N., Athelogou, M., Hessel, H., Brieu, N., Yiğitsoy, M., Zimmermann, J., Baatz, M., Büchner, A., Stief, C. G., Kirchner, T., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-22564-7 · Cited by 31

Stain Style Transfer of Histopathology Images via Structure-Preserved Generative Learning

Liang, H., Plataniotis, K. N., & Li, X. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61598-7_15 · Cited by 30

Classification of colorectal tissue images from high throughput tissue microarrays by ensemble deep learning methods

Nguyen, H.-G., Blank, A., Dawson, H., Lugli, A., & Zlobec, I. (2021) · Scientific Reports · doi:10.1038/s41598-021-81352-y · Cited by 29

Style Normalization In Histology With Federated Learning

Ke, J., Shen, Y., & Lu, Y. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434078 · Cited by 30

Deep Learning–Enabled Diagnosis of Liver Adenocarcinoma

Albrecht, T., Rossberg, A., Albrecht, J. D., Nicolay, J. P., Straub, B. K., Gerber, T. S., Albrecht, M., Brinkmann, F., Charbel, A., Schwab, C., et al. (2023) · Gastroenterology · doi:10.1053/j.gastro.2023.07.026 · Cited by 31

BioFusionNet: Deep learning-based survival risk stratification in ER+ breast cancer through multifeature and multimodal data fusion

Mondol, R. K., Millar, E. K. A., Sowmya, A., & Meijering, E. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3418341 · Cited by 29

A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma

Wessels, F., Schmitt, M., Krieghoff‐Henning, E., Nientiedt, M., Waldbillig, F., Neuberger, M., KRIEGMAIR, M. C., Kowalewski, K., Worst, T. S., Steeg, M., et al. (2023) · World Journal of Urology · doi:10.1007/s00345-023-04489-7 · Cited by 29

A Novel Deep Learning Algorithm for Human Papillomavirus Infection Prediction in Head and Neck Cancers Using Routine Histology Images

Wang, R., Khurram, S. A., Walsh, H., Young, L. S., & Rajpoot, N. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100320 · Cited by 28

A Hybrid Convolutional and Recurrent Deep Neural Network for Breast Cancer Pathological Image Classification

Yan, R., Ren, F., Wang, Z., Wang, L., Ren, Y., Liu, Y., Rao, X., Zheng, C.-H., & Zhang, F. (2018) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621429 · Cited by 32

Stain Removal Through Color Normalization of Haematoxylin and Eosin Images: A Review

Lakshmanan, B., Anand, S., & Jenitha, T. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1362/1/012108 · Cited by 29

Evaluating the Microsatellite Instability of Colorectal Cancer Based on Multimodal Deep Learning Integrating Histopathological and Molecular Data

Qiu, W., Yang, J., Wang, B., Yang, M., Tian, G., Wang, P., & Yang, J. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.925079 · Cited by 31

CycleGAN for virtual stain transfer: Is seeing really believing?

Vasiljević, J., Nisar, Z., Feuerhake, F., Wemmert, C., & Lampert, T. A. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102420 · Cited by 29

The role of unpaired image-to-image translation for stain color normalization in colorectal cancer histology classification

Altini, N., Marvulli, T. M., Zito, F. A., Caputo, M., Tommasi, S., Azzariti, A., Brunetti, A., Prencipe, B., Mattioli, E., Summa, S. D., et al. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107511 · Cited by 29

Whole Slide Imaging-Based Prediction of TP53 Mutations Identifies an Aggressive Disease Phenotype in Prostate Cancer

Pizurica, M., Larmuseau, M., der Eecken, K. V., van Brienen, L. D. S., Carrillo‐Pérez, F., Isphording, S., Lumen, N., Dorpe, J. V., Ost, P., Verbeke, S., et al. (2023) · Cancer Research · doi:10.1158/0008-5472.can-22-3113 · Cited by 30

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

Survival prediction of stomach cancer using expression data and deep learning models with histopathological images

Wei, T., Yuan, X., Gao, R., Johnston, L. J., Zhou, J. N., Wang, Y., Kong, W., Xie, Y., Zhang, Y., Xu, D., et al. (2022) · Cancer Science · doi:10.1111/cas.15592 · Cited by 30

Critical evaluation of artificial intelligence as a digital twin of pathologists for prostate cancer pathology

Eminağa, O., Abbas, M., Kunder, C. A., Tolkach, Y., Han, R., Brooks, J. D., Nolley, R., Semjonow, A., Boegemann, M., West, R. B., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-55228-w · Cited by 32

Ensembling Neural Networks for Digital Pathology Images Classification and Segmentation

Pimkin, A., Makarchuk, G., Кондратенко, В., Pisov, M., Krivov, E., & Belyaev, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93000-8_100 · Cited by 29

Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach

Ma, B., Guo, Y., Hu, W.-A., Yuan, F., Zhu, Z., Yu, Y., & Zou, H. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.572372 · Cited by 30

SA-GAN: Stain Acclimation Generative Adversarial Network for Histopathology Image Analysis

Kausar, T., Kausar, A., Ashraf, M. A., Siddique, M. F., Wang, M., Sajid, M., Siddique, M. Z., Haq, A. U., & Riaz, I. (2021) · Applied Sciences · doi:10.3390/app12010288 · Cited by 29

histolab: A Python library for reproducible Digital Pathology preprocessing with automated testing

Marcolini, A., Bussola, N., Arbitrio, E., Amgad, M. T., Jurman, G., & Furlanello, C. (2022) · SoftwareX · doi:10.1016/j.softx.2022.101237 · Cited by 28

Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types

Cisternino, F., Ometto, S., Chatterjee, S., Giacopuzzi, E., Levine, A. P., & Glastonbury, C. A. (2024) · Nature Communications · doi:10.1038/s41467-024-50317-w · Cited by 27

Weakly supervised segmentation on neural compressed histopathology with self-equivariant regularization

Chikontwe, P., Sung, H. J., Jeong, J., Kim, M., Go, H., Nam, S. J., & Park, S. H. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102482 · Cited by 28

PPsNet: An improved deep learning model for microsatellite instability high prediction in colorectal cancer from whole slide images

Lou, J., Xu, J., Zhang, Y., Sun, Y., Fang, A., Liu, J., Mur, L. A. J., & Ji, B. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107095 · Cited by 27

Squamous Cell Carcinoma of Skin Cancer Margin Classification From Digital Histopathology Images Using Deep Learning

Wako, B. D., Dese, K., Ulfata, R. E., Nigatu, T. A., Turunbedu, S. K., & Kwa, T. (2022) · Cancer Control · doi:10.1177/10732748221132528 · Cited by 27

Stain normalization using score-based diffusion model through stain separation and overlapped moving window patch strategies

Jeong, J., Kim, K., Nam, Y., Cho, C. E., Go, H., & Kim, N. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106335 · Cited by 30

Color normalization of faded H&E-stained histological images using spectral matching

Tosta, T. A. A., de Faria, P. R., Neves, L. A., & do Nascimento, M. Z. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103344 · Cited by 27

Cross-species variability in lobular geometry and cytochrome P450 hepatic zonation: insights into CYP1A2, CYP2D6, CYP2E1 and CYP3A4

Albadry, M., Küttner, J., Grzegorzewski, J., Dirsch, O., Kindler, E. M., Klopfleisch, R., Liška, V., Moulisová, V., Nickel, S., Pálek, R., et al. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2024.1404938 · Cited by 26

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Tripathi, A. G., Waqas, A., Schabath, M. B., Yılmaz, Y., & Rasool, G. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02003-4 · Cited by 37

Ensembled deep convolution neural network-based breast cancer classification with misclassification reduction algorithms

Murtaza, G., Shuib, L. M., Wahab, A. W. A., Mujtaba, G., & Raza, G. M. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08692-1 · Cited by 26

Bayesian K-SVD for H and E blind color deconvolution. Applications to stain normalization, data augmentation and cancer classification

Pérez-Bueno, F., Serra, J. G., Miguel Vega, B., Javier Mateos, A., Molina, R., & Katsaggelos, A. K. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2022.102048 · Cited by 26

Optimization of deep learning models for the prediction of gene mutations using unsupervised clustering

Chen, Z., Li, X., Yang, M., Zhang, H., & Xu, S. X. (2022) · The Journal of Pathology Clinical Research · doi:10.1002/cjp2.302 · Cited by 33

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Li, B., Nelson, M. S., Savari, O., Loeffler, A. G., & Eliceiri, K. W. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100158 · Cited by 27

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Lee, R. Y., Ng, C. W., Rajapakse, M. P., Ang, N., Yeong, J. P.-S., & Lau, M. C. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1172314 · Cited by 28

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Trahearn, N., Snead, D. R. J., Cree, I. A., & Rajpoot, N. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2081933 · Cited by 26

Color correction for automatic fibrosis quantification in liver biopsy specimens

Murakami, Y., Abe, T., Hashiguchi, A., Yamaguchi, M., Saito, A., & Sakamoto, M. (2013) · Journal of Pathology Informatics · doi:10.4103/2153-3539.124009 · Cited by 26

Automated epidermis segmentation in histopathological images of human skin stained with hematoxylin and eosin

Kłeczek, P., Dyduch, G., Jaworek-Korjakowska, J., & Tadeusiewicz, R. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2249018 · Cited by 26

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Vizcarra, J. C., Place, R., Li, T., Gutman, D. A., & Wang, M. D. (2019) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3307339.3342166 · Cited by 26

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Sauter, D., Lodde, G., Nensa, F., Schadendorf, D., Livingstone, E., & Kukuk, M. (2022) · Sensors · doi:10.3390/s22145346 · Cited by 26

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Klein, S., Wuerdemann, N., Demers, I., Kopp, C., Quantius, J., Charpentier, A., Tolkach, Y., Brinker, K., Sharma, S. J., George, J., et al. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00901-z · Cited by 23

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Liu, S., Amgad, M. T., More, D., Rathore, M. A., Salgado, R., & Cooper, L. (2024) · npj Breast Cancer · doi:10.1038/s41523-024-00663-1 · Cited by 25

Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor types

Loeffler, C. M. L., Nahhas, O. S. M. E., Muti, H. S., Carrero, Z. I., Seibel, T., van Treeck, M., Çifçi, D., Gustav, M., Bretz, K., Gaisa, N. T., et al. (2024) · BMC Biology · doi:10.1186/s12915-024-02022-9 · Cited by 39

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Huang, Q., Wu, S., Ou, Z., & Gao, Y. (2025) · Intelligent Oncology · doi:10.1016/j.intonc.2025.03.004 · Cited by 29

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Dabass, M., Vashisth, S., & Vig, R. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100784 · Cited by 25

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Ding, M., Qu, A., Zhong, H., & Liang, H. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669476 · Cited by 25

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Chen, D., Lai, J., Cheng, J., Fu, M., Lin, L., Chen, F., Huang, R. H., Chen, J., Lu, J., Chen, Y., et al. (2023) · iScience · doi:10.1016/j.isci.2023.106246 · Cited by 23

Deep learning trained on lymph node status predicts outcome from gastric cancer histopathology: a retrospective multicentric study

Muti, H. S., Röcken, C., Behrens, H., Loeffler, C. M. L., Reitsam, N. G., Grosser, B., Märkl, B., Stange, D. E., Jiang, X., Veldhuizen, G. P., et al. (2023) · European Journal of Cancer · doi:10.1016/j.ejca.2023.113335 · Cited by 26

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Hewitt, K. J., Loeffler, C. M. L., Muti, H. S., Berghoff, A. S., Eisenlöffel, C., van Treeck, M., Carrero, Z. I., Nahhas, O. S. M. E., Veldhuizen, G. P., Heuer, S., et al. (2023) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdad139 · Cited by 26

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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

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Hoque, M. Z., Keskinarkaus, A., Nyberg, P., & Seppänen, T. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101997 · Cited by 153

Image normalization for quantitative immunohistochemistry in digital pathology

Eycke, Y. V., Allard, J., Derock, M., Salmon, I. J., Debeir, O., & Decaestecker, C. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493386 · Cited by 2

Structure-Preserving Stain Transfer for Histopathology Images: A Technical NotePreprint

Akshat, G. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.22092811

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