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Digital pathology and artificial intelligence
Open accessAbstract
In modern clinical practice, digital pathology has a crucial role and is increasingly a technological requirement in the scientific laboratory environment. The advent of whole-slide imaging, availability of faster networks, and cheaper storage solutions has made it easier for pathologists to manage digital slide images and share them for clinical use. In parallel, unprecedented advances in machine learning have enabled the synergy of artificial intelligence and digital pathology, which offers image-based diagnosis possibilities that were once limited only to radiology and cardiology. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enable true utilisation and integration of knowledge that is beyond human limits and boundaries, and we believe there is clear potential for artificial intelligence breakthroughs in the pathology setting. In this Review, we discuss advancements in digital slide-based image diagnosis for cancer along with some challenges and opportunities for artificial intelligence in digital pathology.
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“The development of deep learning methods has expanded the applications of digital pathology in education, clinical diagnosis, image analysis, and integration with other clinical diagnostic methods [94].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [94]
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“These datasets have enabled the utilization of AI techniques in various downstream tasks, such as sample classification, prognosis prediction, image lesion detection, image segmentation, and others [206], which have demonstrated higher precision and efficacy in clinical settings [94].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [94]
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“This includes the storage of patient data in electronic medical record (EMR) systems, the digitization of radiology and pathology imaging (3), and the increasing adoption of routine genomic profiling (4).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [3]
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
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
Conditional Generative Adversarial Nets
Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863
Image Style Transfer Using Convolutional Neural Networks
Gatys, L. A., Ecker, A. S., & Bethge, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.265 · Cited by 6,025
Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial NetworksPreprint
Zhu, J.-Y., Park, T., Isola, P., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.10593 · Cited by 2,278
Monte Carlo and quasi-Monte Carlo methods
Caflisch, R. E. (1998) · Acta Numerica · doi:10.1017/s0962492900002804 · Cited by 1,783
Schaeffer, S. E. (2007) · Computer Science Review · doi:10.1016/j.cosrev.2007.05.001 · Cited by 1,679
A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology
Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., & Sethi, A. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2677499 · Cited by 1,160
Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
Litjens, G., Sánchez, C. I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., Hulsbergen - van de Kaa, C., Bult, P., van Ginneken, B., & van der Laak, J. (2016) · Scientific Reports · doi:10.1038/srep26286 · Cited by 1,069
Machine Learning Methods for Histopathological Image Analysis
Komura, D., & Ishikawa, S. (2018) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2018.01.001 · Cited by 937
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
Artificial Intelligence and Digital Pathology: Challenges and Opportunities
Tizhoosh, H. R., & Pantanowitz, L. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_53_18 · Cited by 544
Xing, F., & Yang, L. (2016) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2016.2515127 · Cited by 527
A Practical Guide to Whole Slide Imaging: A White Paper From the Digital Pathology Association
Zarella, M. D., Bowman, D., Aeffner, F., Farahani, N., Xthona, A., Absar, S. F., Parwani, A. V., Bui, M. M., & Hartman, D. J. (2018) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2018-0343-ra · Cited by 431
Self-citationWhole slide imaging in pathology: advantages, limitations, and emerging perspectives
Pantanowitz, L., Farahani, N., & Parwani, A. V. (2015) · Pathology and Laboratory Medicine International · doi:10.2147/plmi.s59826 · Cited by 387
Self-citationEthics in artificial intelligence: introduction to the special issue
Dignum, V. (2018) · Ethics and Information Technology · doi:10.1007/s10676-018-9450-z · Cited by 379
A region growing and merging algorithm to color segmentation
Trémeau, A., & Borel, N. (1997) · Pattern Recognition · doi:10.1016/s0031-3203(96)00147-1 · Cited by 373
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 371
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 361
An Automatic Learning-Based Framework for Robust Nucleus Segmentation
Xing, F., Xie, Y., & Yang, L. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2481436 · Cited by 357
Significance of immunohistochemistry in breast cancer
Zaha, D. C. (2014) · World Journal of Clinical Oncology · doi:10.5306/wjco.v5.i3.382 · Cited by 316
Song, Y., Zhang, L., Chen, S., Ni, D., Lei, B., & Wang, T. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2430895 · Cited by 272
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 235
Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology
Heindl, A., Nawaz, S., & Yuan, Y. (2015) · Laboratory Investigation · doi:10.1038/labinvest.2014.155 · Cited by 220
DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks
Li, C., Wang, X., Liu, W., & Latecki, L. J. (2018) · Medical Image Analysis · doi:10.1016/j.media.2017.12.002 · Cited by 190
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 189
Thorstenson, S., Molin, J., & Lundström, C. F. (2014) · Journal of Pathology Informatics · doi:10.4103/2153-3539.129452 · Cited by 175
Cruz-Roa, Á., Gilmore, H. L., Basavanhally, A. N., Feldman, M. D., Ganesan, S., Shih, N. N. C., Tomaszewski, J., Madabhushi, A., & Gonzalez, F. A. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196828 · Cited by 151
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
Natrajan, R., Sailem, H., Mardakheh, F. K., Arias-García, M., Tape, C. J., Dowsett, M., Bakal, C., & Yuan, Y. (2016) · PLoS Medicine · doi:10.1371/journal.pmed.1001961 · Cited by 134
DeepFocus: Detection of out-of-focus regions in whole slide digital images using deep learning
Şenaras, Ç., Niazi, M. K. K., Lozanski, G., & Gürcan, M. N. (2018) · PLoS ONE · doi:10.1371/journal.pone.0205387 · Cited by 119
Self-citationChappelow, J., Bloch, B., Rofsky, N. M., Genega, E. M., Lenkinski, R. E., DeWolf, W. C., & Madabhushi, A. (2011) · Medical Physics · doi:10.1118/1.3560879 · Cited by 115
Computational Pathology: An Emerging Definition
Louis, D. N., Gerber, G. K., Baron, J. M., Bry, L., Dighe, A. S., Getz, G. A., Higgins, J. M., Kuo, F. C., Lane, W. J., Michaelson, J. S., et al. (2014) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2014-0034-ed · Cited by 97
Sertel, O., Lozanski, G., & Gürcan, M. N. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2055058 · Cited by 93
Automated Detection of DCIS in Whole-Slide H&E Stained Breast Histopathology Images
Bejnordi, B. E., Balkenhol, M. C. A., Litjens, G. J. S., Holland, R., Bult, P., Karssemeijer, N., & van der Laak, J. A. W. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2550620 · Cited by 89
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
Tabata, K., Mori, I., Sasaki, T., Itoh, T., Shiraishi, T., Yoshimi, N., Maeda, I., Harada, O., Taniyama, K., Taniyama, D., et al. (2017) · Pathology International · doi:10.1111/pin.12590 · Cited by 60
Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning
Niazi, M. K. K., Tavolara, T. E., Arole, V., Hartman, D. J., Pantanowitz, L., & Gürcan, M. N. (2018) · PLoS ONE · doi:10.1371/journal.pone.0195621 · Cited by 51
Self-citationAn Automated Blur Detection Method for Histological Whole Slide Imaging
Lopez, X. M., D'Andrea, E., Barbot, P., Bridoux, A.-S., Rorive, S., Salmon, I. J., Debeir, O., & Decaestecker, C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0082710 · Cited by 44
A quantitative approach to evaluate image quality of whole slide imaging scanners
Shrestha, P., Kneepkens, R. E. J., Vrijnsen, J., Vossen, D. L. J., Abels, E., & Hulsken, B. (2016) · Journal of Pathology Informatics · doi:10.4103/2153-3539.197205 · Cited by 37
(2017) · Cited by 35
Das, H., Wang, Z., Niazi, M. K. K., Aggarwal, R., Lu, J., Kanji, S., Das, M., Joseph, M., Gürcan, M. N., & Vittorio Cristini, C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061398 · Cited by 31
Self-citationDigital slide viewing for primary reporting in gastrointestinal pathology: a validation study
Loughrey, M. B., Kelly, P. J., Houghton, O. P., Coleman, H. G., Houghton, J., Carson, A., Salto‐Tellez, M., & Hamilton, P. W. (2015) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-015-1780-1 · Cited by 27
Niazi, M. K. K., Lin, Y., Liu, F., Ashok, A., Marcellin, M. W., Tozbikian, G. H., Gürcan, M. N., & Bilgin, A. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.09.002 · Cited by 25
Self-citationKornaropoulos, E., Niazi, M. K. K., Lozanski, G., & Gürcan, M. N. (2013) · Cytometry Part A · doi:10.1002/cyto.a.22432 · Cited by 23
Self-citationHot spot detection for breast cancer in Ki-67 stained slides: image dependent filtering approach
Niazi, M. K. K., Downs-Kelly, E. P., & Gürcan, M. N. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2045586 · Cited by 17
Self-citationTransfer Learning from Nucleus Detection to Classification in Histopathology Images
Yousefi, S., & Nie, Y. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759469 · Cited by 15
Niazi, M. K. K., Abas, F. S., Şenaras, Ç., Pennell, M., Sahiner, B., Chen, W., Opfer, J. E., Hasserjian, R. P., Louissaint, A., Shana’ah, A., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196547 · Cited by 12
Self-citationHierarchical patch-based co-registration of differently stained histopathology slides
Yiğitsoy, M., & Schmidt, G. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2254266 · Cited by 7
Hotspot detection in pancreatic neuroendocrine tumors: density approximation by α-shape maps
Niazi, M. K. K., Hartman, D. J., Pantanowitz, L., & Gürcan, M. N. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2217687 · Cited by 6
Self-citationNiazi, M. K. K., Tavolara, T. E., Arole, V., Parwani, A. V., Lee, C. Q. E., & Gürcan, M. N. (2018) · The Journal of Urology · doi:10.1016/j.juro.2018.02.1838 · Cited by 6
Self-citationSertel, O., Dogdas, B., Chiu, C.-S., & Gürcan, M. (2010) · IEEE International Symposium on Biomedical Imaging · doi:10.1109/isbi.2010.5490266 · Cited by 5
Niazi, M. K. K., Chung, J. H., Heaton-Johnson, K. J., Martínez, D., Castellanos, R., Irwin, M. S., Master, S. R., Pawel, B., Gürcan, M. N., & Weiser, D. A. (2017) · Pediatric and Developmental Pathology · doi:10.1177/1093526617698603 · Cited by 5
Self-citationNiazi, M. K. K., Tavolara, T. E., Arole, V., Parwani, A. V., Lee, C. T., & Gürcan, M. N. (2018) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2294552 · Cited by 5
Self-citationColorectal tumor identification by transferring knowledge from pan-cytokeratin to H&E
Tavolara, T. E., Niazi, M. K. K., Chen, W. R., Frankel, W. L., & Gürcan, M. N. (2019) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2513014 · Cited by 5
Self-citationJohnson, S., Doyle, S. T., & Brandwein, M. S. (2018) · SPIE Medical Imaging: Digital Pathology · doi:10.1117/12.2293962 · Cited by 2
Research works printed but not linked: 1
- Niazi, 2018, Automated T1 bladder risk stratification based on depth of lamina propria invasion from H&E tissue biopsies: a deep learning approach, SPIE Medical Imaging, 10581, 105810H1
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Metadata
- Authors
- Niazi, M. K. K., Parwani, A. V., & Gurcan, M. N.
- Affiliations
- Atrium Health Wake Forest Baptist (US)
- The Ohio State University (US)
- Corresponding author
- Niazi, Muhammad Khalid Khan
- Venue
- The Lancet Oncology
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 20
- Issue
- 5
- Pages
- e253-e261
- ISSN
- 1470-2045 (ISSN-L)
- PMC
- PMC8711251
- OpenAlex
- W2943370629
- Cited by
- 1,232
- References
- 61
- Citations by year
- 2026: 164 · 2025: 204 · 2024: 236 · 2023: 209 · 2022: 175 · 2021: 138 · 2020: 91 · 2019: 12
- FWCI
- 6.83
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- The Lancet Oncology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute and The Ohio State University Comprehensive Cancer Center Intramural Research (R01CA134451)
- Metin N Gurcan (co-principal investigator) and Gerard Lozanski (U24CA199374)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- digital pathology, artificial intelligence, whole slide imaging, computer-aided diagnosis, cancer diagnosis
- MeSH terms
- Artificial Intelligence, Diagnosis, Computer-Assisted, Humans, Image Interpretation, Computer-Assisted, Microscopy, Neoplasms, Pathology, Predictive Value of Tests, Prognosis, Reproducibility of Results, Workflow
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