Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

A survey on deep learning in medical image analysis

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332

The cancer genome atlas pan-cancer analysis project

The Cancer Genome Atlas Research Network, Weinstein, J. N., Collisson, E. A., Mills, G. B., Shaw, K. R. M., Ozenberger, B. A., Ellrott, K., Shmulevich, I., Sander, C., & Stuart, J. M. (2013) · Nature Genetics · doi:10.1038/ng.2764 · Cited by 9,092

Histological Grading and Prognosis in Breast Cancer

Bloom, H. J. G., & Richardson, W. (1957) · British Journal of Cancer · doi:10.1038/bjc.1957.43 · Cited by 2,962

Best practices for convolutional neural networks applied to visual document analysis

Simard, P. Y., Steinkraus, D., & Platt, J. C. (2005) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2003.1227801 · Cited by 2,862

Quantification of histochemical staining by color deconvolution.

Ruifrok, A. C. C., & Johnston, D. A. (2001) · PubMed · Cited by 2,029

Pathological prognostic factors in breast cancer

Elston, C. W. (1999) · Critical Reviews in Oncology/Hematology · doi:10.1016/s1040-8428(99)00034-7 · Cited by 1,744

Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks

Cireşan, D. C., Giusti, A., Gambardella, L. M., & Schmidhuber, J. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_51 · Cited by 1,536

A method for normalizing histology slides for quantitative analysis

Macenko, M., Niethammer, M., Marron, J. S., Borland, D., Woosley, J. T., Guan, X., Schmitt, C., & Thomas, N. E. (2009) · 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro · doi:10.1109/isbi.2009.5193250 · Cited by 1,482

A Comparison of PAM50 Intrinsic Subtyping with Immunohistochemistry and Clinical Prognostic Factors in Tamoxifen-Treated Estrogen Receptor–Positive Breast Cancer

Nielsen, T. O., Parker, J. S., Leung, S. C. Y., Voduc, D. K., Ebbert, M., Vickery, T. L., Davies, S. R., Snider, J., Stijleman, I. J., Reed, J. P., et al. (2010) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-10-1282 · Cited by 825

Spatio-Temporal Mitosis Detection in Time-Lapse Phase-Contrast Microscopy Image Sequences: A Benchmark

Su, Y., Lu, Y., Liu, J., Chen, M., & Liu, A.-A. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3052854 · Cited by 29

Lung cancer lesion detection in histopathology images using graph‐based sparse PCA network

Ram, S., Tang, W., Bell, A. J., Pal, R., Spencer, C., Buschhaus, A., Hatt, C. R., di Magliano, M. P., Rehemtulla, A., Rodrı́guez, J. J., et al. (2023) · Neoplasia · doi:10.1016/j.neo.2023.100911 · Cited by 30

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

CCF-GNN: A Unified Model Aggregating Appearance, Microenvironment, and Topology for Pathology Image Classification

Wang, H., Huang, G., Zhao, Z., Liang Cheng, C., Juncker‐Jensen, A., Nagy, M. L., Lü, X., Zhang, X., & Chen, D. Z. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3249343 · Cited by 30

Generalization of vision pre-trained models for histopathology

Sikaroudi, M., Hosseini, M., González, R., Rahnamayan, S., & Tizhoosh, H. R. (2023) · Scientific Reports · doi:10.1038/s41598-023-33348-z · Cited by 30

Performance and limitations of a supervised deep learning approach for the histopathological Oxford Classification of glomeruli with IgA nephropathy

Altini, N., Rossini, M., Túrkevi-Nagy, S., Pesce, F., Pontrelli, P., Prencipe, B., Berloco, F., Seshan, S. V., Gibier, J., Pedraza, A., et al. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107814 · Cited by 27

Automatic detection of squamous cell carcinoma metastasis in esophageal lymph nodes using semantic segmentation

Pan, Y., Sun, Z., Wang, W., Yang, Z., Jia, J., Feng, X., Wang, Y., Fang, Q., Li, J., Dai, H., et al. (2020) · Clinical and Translational Medicine · doi:10.1002/ctm2.129 · Cited by 27

Strategies to Reduce the Expert Supervision Required for Deep Learning-Based Segmentation of Histopathological Images

Eycke, Y. V., Foucart, A., & Decaestecker, C. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00222 · Cited by 29

Semi-HIC: A novel semi-supervised deep learning method for histopathological image classification

Su, L., Liu, Y., Wang, M., & Li, A. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104788 · Cited by 27

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

Domain Adaptive Box-Supervised Instance Segmentation Network for Mitosis Detection

Li, Y., Xue, Y., Liang-fu, L., Zhang, X., & Qian, X. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3165518 · Cited by 29

Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer

Balkenhol, M. C. A., Bult, P., Tellez, D., Vreuls, W., Clahsen, P. C., Ciompi, F., & van der Laak, J. A. W. M. (2019) · Cellular Oncology · doi:10.1007/s13402-019-00445-z · Cited by 25

Multimarginal Wasserstein Barycenter for Stain Normalization and Augmentation

Nadeem, S., Hollmann, T. J., & Tannenbaum, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59722-1_35 · Cited by 26

Teacher-student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides

Jin, D., Liang, S., Shmatko, A., Arnold, A., Horst, D. A., Grünewald, T. G. P., Gerstung, M., & Bai, X. (2024) · Nature Communications · doi:10.1038/s41467-024-46764-0 · Cited by 27

Prototype transfer generative adversarial network for unsupervised breast cancer histology image classification

Wang, D., Chen, Z., & Zhao, H. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102713 · Cited by 26

Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet

Zhu, C., Mei, K., Peng, T., Luo, Y., Liu, J., Wang, Y., & Jin, M. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.04.154 · Cited by 24

Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer

Mercan, C., Balkenhol, M. C. A., Salgado, R., Sherman, M. E., Vielh, P. R., Vreuls, W., Polónia, A., Horlings, H. M., Weichert, W., Carter, J. M., et al. (2022) · npj Breast Cancer · doi:10.1038/s41523-022-00488-w · Cited by 26

Bias reduction in representation of histopathology images using deep feature selection

Bidgoli, A. A., Rahnamayan, S., Dehkharghanian, T., Grami, A., & Tizhoosh, H. R. (2022) · Scientific Reports · doi:10.1038/s41598-022-24317-z · Cited by 26

Deep learning‐based classification and spatial prognosis risk score on whole‐slide images of lung adenocarcinoma

Ding, H., Feng, Y., Huang, X., Xu, J., Zhang, T., Liang, Y., Wang, H., Chen, B., Mao, Q., Xia, W., et al. (2023) · Histopathology · doi:10.1111/his.14918 · Cited by 25

Label-free multimodal optical biopsy reveals biomolecular and morphological features of diabetic kidney tissue in 2D and 3D

Fung, A. A., Li, Z., Boote, C., Markov, P., Gaut, J. P., Jain, S. R., & Shi, L. (2025) · Nature Communications · doi:10.1038/s41467-025-59163-w · Cited by 22

MiNuGAN: Dual Segmentation of Mitoses and Nuclei Using Conditional GANs on Multi-center Breast H&E Images

Razavi, S., Khameneh, F. D., Nouri, H., Androutsos, D., Done, S. J., & Khademi, A. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100002 · Cited by 22

No pixel-level annotations needed

van der Laak, J. A. W. M., Ciompi, F., & Litjens, G. J. S. (2019) · Nature Biomedical Engineering · doi:10.1038/s41551-019-0472-6 · Cited by 22

DeepHistoClass: A Novel Strategy for Confident Classification of Immunohistochemistry Images Using Deep Learning

Ghoshal, B., Hikmet, F., Pineau, C., Tucker, A., & Lindskog, C. (2021) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2021.100140 · Cited by 22

Comparison of consecutive and restained sections for image registration in histopathology

Lotz, J., Weiss, N., van der Laak, J. A. W. M., & Heldmann, S. (2023) · Journal of medical imaging · doi:10.1117/1.jmi.10.6.067501 · Cited by 20

Developing a Qualification and Verification Strategy for Digital Tissue Image Analysis in Toxicological Pathology

Żuraw, A., Staup, M. A., Klopfleisch, R., Aeffner, F., Brown, D. L., Westerling, T., & Rudmann, D. G. (2020) · Toxicologic Pathology · doi:10.1177/0192623320980310 · Cited by 20

Contrastive learning-based computational histopathology predict differential expression of cancer driver genes

Huang, H., Zhou, G., Liu, X., Deng, L., Wu, C., Zhang, D., & Liu, H. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac294 · Cited by 17

A machine learning model for separating epithelial and stromal regions in oral cavity squamous cell carcinomas using H&E-stained histology images: A multi-center, retrospective study

Wu, Y., Koyuncu, C., Toro, P., Corredor, G., Feng, Q., Buzzy, C. A., Old, M. O., Teknos, T. N., Connelly, S., Jordan, R. C., et al. (2022) · Oral Oncology · doi:10.1016/j.oraloncology.2022.105942 · Cited by 19

Bias reduction using combined stain normalization and augmentation for AI-based classification of histological images

Franchet, C., Schwob, R., Bataillon, G., Syrykh, C., Péricart, S., Frénois, F.-X., Penault-Llorca, F. M., Lacroix‐Triki, M., Arnould, L., Lemonnier, J., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108130 · Cited by 20

Rheumatoid Arthritis Synovial Inflammation Quantification Using Computer Vision

Guan, S., Mehta, B., Slater, D., Thompson, J. R., DiCarlo, E., Pannellini, T., Pearce‐Fisher, D., Zhang, F., Raychaudhuri, S., Hale, C., et al. (2022) · ACR Open Rheumatology · doi:10.1002/acr2.11381 · Cited by 19

DASGAN -- Joint Domain Adaptation and Segmentation for the Analysis of Epithelial Regions in Histopathology PD-L1 ImagesPreprint

Kapil, A., Wiestler, T., Lanzmich, S., Silva, A., Steele, K. E., Rebelatto, M. C., Schmidt, G., & Brieu, N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.11118 · Cited by 19

Explainable nucleus classification using Decision Tree Approximation of Learned Embeddings

Amgad, M. T., Atteya, L. A., Hussein, H., Mohammed, K. H., Hafiz, E. O. A., Elsebaie, M. A., Mobadersany, P., Manthey, D., Gutman, D. A., Elfandy, H., et al. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab670 · Cited by 18

Automated Detection and Scoring of Tumor-Infiltrating Lymphocytes in Breast Cancer Histopathology Slides

Yosofvand, M., Khan, S. Y., Dhakal, R., Nejat, A., Moustaïd‐Moussa, N., Rahman, R. L., & Moussa, H. M. (2023) · Cancers · doi:10.3390/cancers15143635 · Cited by 18

Channel Boosted Convolutional Neural Network for Classification of Mitotic Nuclei using Histopathological Images

Aziz, A., Sohail, A., Fahad, L. G., Burhan, M., Wahab, N., & Khan, A. (2020) · International Bhurban Conference on Applied Sciences and Technology (IBCAST) · doi:10.1109/ibcast47879.2020.9044583 · Cited by 19

HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images

van Rijthoven, M., Balkenhol, M., Siliņa, K., van der Laak, J., & Ciompi, F. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101890 · Cited by 16

Rethinking ImageNet Pre-training for Computational Histopathology

Ray, I., Raipuria, G., & Singhal, N. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871687 · Cited by 15

Influence of Inter-Annotator Variability on Automatic Mitotic Figure Assessment

Wilm, F., Bertram, C., Marzahl, C., Bartel, A., Donovan, T., Assenmacher, C., Becker, K., Bennett, M., Corner, S. M., Cossic, B., et al. (2021) · Informatik aktuell · doi:10.1007/978-3-658-33198-6_56 · Cited by 15

Deep feature based cross-slide registration

Awan, R., Raza, S. E. A., Lotz, J., Weiss, N., & Rajpoot, N. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2022.102162 · Cited by 14

A deep learning framework deploying segment anything to detect pan-cancer mitotic figures from haematoxylin and eosin-stained slides

Shen, Z., Simard, M., Brand, D. H., Andrei, V., Khader, A. A., Oumlil, F., Trevers, K. E., Butters, T., Haefliger, S., Kara, E., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-07398-6 · Cited by 14

Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends

Elazab, N., Soliman, H., El–Sappagh, S., Islam, S. M. R., & Elmogy, M. M. (2020) · RePEc: Research Papers in Economics · Cited by 14

Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm

Sturm, B., Creytens, D., Smits, J., Ooms, A. H. A. G., Eijken, E., Kurpershoek, E., Küsters-Vandevelde, H. V. N., Wauters, C., Blokx, W. A. M., & van der Laak, J. A. W. M. (2022) · Diagnostics · doi:10.3390/diagnostics12020436 · Cited by 13

Pathological Response and Immune Biomarker Assessment in Non-Small-Cell Lung Carcinoma Receiving Neoadjuvant Immune Checkpoint Inhibitors

Rojas, F. R., Parra, E. R., Wistuba, I. I., Haymaker, C., & Soto, L. M. S. (2022) · Cancers · doi:10.3390/cancers14112775 · Cited by 14

Computer-Assisted Annotation of Digital H&E/SOX10 Dual Stains Generates High-Performing Convolutional Neural Network for Calculating Tumor Burden in H&E-Stained Cutaneous Melanoma

Nielsen, P. S., Georgsen, J. B., Vinding, M. S., Østergaard, L. R., & Steiniche, T. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192114327 · Cited by 13

Uninformed Teacher-Student for hard-samples distillation in weakly supervised mitosis localization

Fernandez‐Martín, C., Silva-Rodríguez, J., Kiraz, U., Morales, S., Janssen, E. A. M., & Naranjo, V. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102328 · Cited by 13

An ensemble of deep CNNs for automatic grading of breast cancer in digital pathology images

Sharma, S., Kumar, S., Sharma, M., & Kalkal, A. (2024) · Neural Computing and Applications · doi:10.1007/s00521-023-09368-1 · Cited by 13

RandStainNA++: Enhance Random Stain Augmentation and Normalization Through Foreground and Background Differentiation

Wang, C., Li, S., Ke, J., Zhang, C., & Shen, Y. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3379280 · Cited by 13

DistilIQA: Distilling Vision Transformers for no-reference perceptual CT image quality assessment

Baldeon-Calisto, M., Rivera-Velastegui, F., Lai-Yuen, S. K., Riofrío, D., Pérez, N., Benítez, D. S., & Flores-Moyano, R. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108670 · Cited by 14

Deep learning-based detection and semi-quantitative model for spread through air spaces (STAS) in lung adenocarcinoma

Feng, Y., Ding, H., Huang, X., Zhang, Y., Lu, M., Zhang, T., Wang, H., Chen, Y., Mao, Q., Xia, W., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00664-0 · Cited by 13

Test-Time Image-to-Image Translation Ensembling Improves Out-of-Distribution Generalization in Histopathology

Scalbert, M., Vakalopoulou, M., & Couzinié-Devy, F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16434-7_12 · Cited by 13

Digital Pathology and Artificial Intelligence Applied to Nonclinical Toxicology Pathology—The Current State, Challenges, and Future Directions

Pohlmeyer-Esch, G., Halsey, C., Boisclair, J., Ram, S., Kitz, S., Knight, B., Moulin, P., & Frisk, A.-L. (2025) · Toxicologic Pathology · doi:10.1177/01926233251340622 · Cited by 13

Deep Semi-Supervised Metric Learning with Dual Alignment for Cervical Cancer Cell Detection

Chai, Z., Luo, L., Lin, H., Chen, H., Han, A., & Heng, P. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761652 · Cited by 12

Artificial Intelligence-Based Quantification of Epithelial Proliferation in Mammary Glands of Rats and Oviducts of Göttingen Minipigs

Hvid, H., Skydsgaard, M., Jensen, N. K., Viuff, B. M., Jensen, H. E., Oleksiewicz, M. B., & Kvist, P. H. (2020) · Toxicologic Pathology · doi:10.1177/0192623320950633 · Cited by 12

On Transferability of Histological Tissue Labels in Computational Pathology

Hosseini, M. S., Chan, L., Huang, W., Wang, Y., Hasan, D., Rowsell, C. H., Damaskinos, S., & Plataniotis, K. N. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58526-6_27 · Cited by 12

Predicting the Likelihood of Colorectal Cancer with Artificial Intelligence Tools Using Fourier Transform Infrared Signals Obtained from Tumor Samples

Villamanca, J. J., Hermogino, L. J., Ong, K. D., Paguia, B., Abanilla, L., Lim, A., Angeles, L. M., Espiritu, B., Isais, M., Tomas, R. C., et al. (2022) · Applied Spectroscopy · doi:10.1177/00037028221116083 · Cited by 11

The role of histological stains in aquaculture and fisheries development technology

Eissa, E. H., Gaafar, A. Y., Mathew, R. T., Al-Shahari, E. A., Okon, E. M., Eissa, M. E. H., & El‐Aziz, Y. M. A. (2025) · Aquaculture International · doi:10.1007/s10499-025-01932-x · Cited by 11

Scalable, trustworthy generative model for virtual multi-staining from H&E whole slide images

Ounissi, M., Sarbout, I., Hugot, J., Martinez‐Vinson, C., Berrebi, D. D., & Racoceanu, D. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013516 · Cited by 14

Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

Mehak, S., Ashraf, M. U., Zafar, R., Alghamdi, A. M., Alfakeeh, A. S., Alassery, F., Hamam, H., & Shafiq, M. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.022705 · Cited by 11

Automatic evaluation of graft orientation during Descemet membrane endothelial keratoplasty using intraoperative OCT

Muijzer, M. B., Heslinga, F. G., Couwenberg, F., Noordmans, H. J., Oahalou, A., Pluim, J. P. W., Veta, M., & Wisse, R. P. L. (2022) · Biomedical Optics Express · doi:10.1364/boe.446519 · Cited by 11

Global Research Trends of Artificial Intelligence on Histopathological Images: A 20-Year Bibliometric Analysis

Zhou, W., Deng, Z., Liu, Y., Shen, H., Deng, H., & Xiao, H. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph191811597 · Cited by 13

Interobserver variability in mitotic figure counting for meningioma grading: how can we reduce it?

Sayğın, İ., Çakır, E., & Erçin, M. E. (2019) · Turkish Neurosurgery · doi:10.5137/1019-5149.jtn.26252-19.2 · Cited by 11

Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification

Wilm, F., Benz, M., Bruns, V., Baghdadlian, S., Dexl, J., Hartmann, D., Kuritcyn, P., Weidenfeller, M., Wittenberg, T. C., Merkel, S., et al. (2022) · Journal of medical imaging · doi:10.1117/1.jmi.9.2.027501 · Cited by 11

Improved Domain Generalization for Cell Detection in Histopathology Images via Test-Time Stain Augmentation

Xu, C., Wen, Z., Liu, Z., & Ye, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16434-7_15 · Cited by 11

Deep feature batch correction using ComBat for machine learning applications in computational pathology

Murchan, P., Broin, P. Ó., Baird, A., Sheils, O. M., & Finn, S. P. (2024) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2024.100396 · Cited by 12

DenseNet-FPA: Integrating DenseNet and Flower Pollination Algorithm for Breast Cancer Histopathology Image Classification

Wakili, M. A., Shehu, H. A., Abdollahi, M., Ya’u, B. I., Sharif, M. H., & Kusetoğulları, H. (2025) · IEEE Access · doi:10.1109/access.2025.3599319 · Cited by 10

From imaging a single cell to implementing precision medicine: an exciting new era

Karacosta, L. G. (2021) · Emerging Topics in Life Sciences · doi:10.1042/etls20210219 · Cited by 12

Strategies for Training Stain Invariant CNNS

Lampert, T. A., Merveille, O., Schmitz, J., Forestier, G., Feuerhake, F., & Wemmert, C. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759266 · Cited by 9

Deep Learning Methods for Mitosis Detection in Breast Cancer Histopathological Images: A Comprehensive Review

Dif, N., & Elberrichi, Z. E. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50402-1_17 · Cited by 10

Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&E slide analysis

Ganz, J., Marzahl, C., Ammeling, J., Rosbach, E., Richter, B., Puget, C., Denk, D., Demeter, E., Tăbăran, F., Wasinger, G., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-77244-6 · Cited by 9

Expectations of Artificial Intelligence for Pathology

Regitnig, P., Müller, H., & Holzinger, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50402-1_1 · Cited by 8

Utility of Machine Learning to Detect Cytomegalovirus in Digital Hematoxylin and Eosin–Stained Slides

Post, C. S., Cheng, J., Pantanowitz, L., & Westerhoff, M. A. (2023) · Laboratory Investigation · doi:10.1016/j.labinv.2023.100225 · Cited by 7

From traditional to deep learning approaches in whole slide image registration: A methodological review

Elhaminia, B., Alsalemi, A., Nasir, E. S., Jahanifar, M., Awan, R., Young, L. S., Rajpoot, N., Minhas, F., & Raza, S. E. A. (2025) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2025.100512 · Cited by 8

Integration of geoscience frameworks into digital pathology analysis permits quantification of microarchitectural relationships in histological landscapes

Kendall, T. J., Duff, C. M., Thomson, A. M., & Iredale, J. P. (2020) · Scientific Reports · doi:10.1038/s41598-020-74691-9 · Cited by 7

Cheminformatics and Bioinformatics at the Interface with Systems Biology

Kaushik, A. C., Mehmood, A., Wei, D., Nawab, S., Sahi, S., & Kumar, A. (2023) · Royal Society of Chemistry eBooks · doi:10.1039/9781839166037 · Cited by 7

Addressing chemoresistance with a lipid gemcitabine nanotherapeutic strategy for effective treatment of pancreatic cancer

Hong, J., Xian, S., Zheng, S., Wang, H., & Jiang, D. (2024) · Nano Research · doi:10.1007/s12274-024-6860-6 · Cited by 7

Self-supervised Vision Transformer are Scalable Generative Models for Domain Generalization

Doerrich, S., Salvo, F. D., & Ledig, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72117-5_60 · Cited by 7

Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology

Mazaheri, P., Bidgoli, A. A., Rahnamayan, S., & Tizhoosh, H. R. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110346 · Cited by 11

Stain Normalization and Augmentation in Frequency Space for Histology Analysis

Li, Y., Zhou, H., Liu, N., & Shen, Y. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385658 · Cited by 7

Optimized generative adversarial network based breast cancer diagnosis with wavelet and texture features

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Fick, R., Tayart, B., Bertrand, C., Lang, S. C., Rey, T., Ciompi, F., Tilmant, C., Farre, I., & Hadj, S. B. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9631009 · Cited by 6

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(2023) · Royal Society of Chemistry eBooks · doi:10.1039/9781839166037-00196 · Cited by 4

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Shen, Z., Brand, D. H., Simard, M., Levine, A. P., Hindocha, S., Mistry, T., Oukrif, D., Lopes, A., Begum, R., West, N. P., et al. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.105993 · Cited by 4

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