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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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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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
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
Cited by
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Items 501–600 of 1,000
Solution-free and simplified H&E staining using a hydrogel-based stamping technology
Kim, J.-H., Choi, W., Yoo, D., Kim, M., Cho, H., Sung, H.-J., Choi, G., Uh, J., Kim, J., Go, H., et al. (2023) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2023.1292785 · Cited by 11
Wibawa, M. S., Zhou, J., Wang, R., Huang, Y., Zhan, Z., Chen, X., Lv, X., Young, L. S., & Rajpoot, N. (2023) · Cancers · doi:10.3390/cancers15245789 · Cited by 12
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Huang, J., Bai, X., Qiu, Y., & He, X. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1324222 · Cited by 11
Amin, M. H. J., Fadlalmoula, G. A. A. G. A., Elmahi, M. A. M. E., Alrabee, N. H. K., Hemmeda, L., Awad, M. H., Ahmed, G. E. M., & Ahmed, K. A. H. M. (2024) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000002274 · Cited by 11
Chang, J., & Hatfield, B. S. (2024) · Advances in cancer research · doi:10.1016/bs.acr.2024.05.006 · Cited by 13
Tian, Z., Wang, D., Sun, X., Cui, C., & Wang, H. (2024) · Frontiers in Physiology · doi:10.3389/fphys.2024.1473659 · Cited by 14
Dang, R. R., Kadaikal, B., Abbadi, S. E., Brar, B. R., Sethi, A., & Chigurupati, R. (2025) · Oral and Maxillofacial Surgery · doi:10.1007/s10006-025-01334-6 · Cited by 12
Stagno, F., Mirabile, G., Rizzotti, P., Bottaro, A., Pagana, A., Gangemi, S., & Allegra, A. (2025) · Biomedicines · doi:10.3390/biomedicines13040835 · 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
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Torres, R., Olson, E. M., Homer, R., Martin, D. T., Levene, M. J., Perincheri, S., Sprenkle, P. C., & Humphrey, P. A. (2020) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2020-0037-oa · Cited by 11
Hou, A., Wang, X., Fan, Y., Miao, W., Dong, Y., Tian, X., Zou, J., & Ma, H. (2021) · Journal of Innovative Optical Health Sciences · doi:10.1142/s1793545821420086 · Cited by 11
Lad, J., Serra, S., Quereshy, F. A., Khorasani, M., & Vitkin, I. A. (2022) · Scientific Reports · doi:10.1038/s41598-022-16178-3 · Cited by 11
Ye, Y., Wu, X., Wang, H., Ye, H., Zhao, K., Yao, S., Liu, Z., Zhu, Y., Zhang, Q., & Liang, C. (2023) · Annals of Medicine · doi:10.1080/07853890.2023.2215541 · Cited by 11
Computer‐assisted urine cytology: Faster, cheaper, better?
Ciaparrone, C., Maffei, E., L’Imperio, V., Pisapia, P., Eloy, C., Fraggetta, F., Zeppa, P., & Caputo, A. (2024) · Cytopathology · doi:10.1111/cyt.13412 · Cited by 11
Hassan, L., Saleh, A., Abdel‐Nasser, M., Omer, O. A., & Puig, D. (2020) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2020.10.004 · Cited by 11
PTEN and DNA Ploidy Status by Machine Learning in Prostate Cancer
Cyll, K., Kleppe, A., Kalsnes, J., Vlatkovic, L., Pradhan, M., Kildal, W., Tobin, K. A. R., Reine, T. M., Wæhre, H., Brennhovd, B., et al. (2021) · Cancers · doi:10.3390/cancers13174291 · Cited by 11
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Pan, S., Fu, Y., Chen, P., Liu, J., Liu, W., Wang, X., Cai, G., Yin, Z., Wu, J., Tang, L., et al. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182010798 · Cited by 11
On the Road to Digital Pathology in Denmark—National Survey and Interviews
Smith, J. S., Johnsen, S., Zeuthen, M. C., Thomsen, L. K., Marcussen, N., Hansen, S. L., & Jensen, C. L. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-022-00638-3 · Cited by 11
Cross-Attention-Based Saliency Inference for Predicting Cancer Metastasis on Whole Slide Images
Su, Z., Rezapour, M., Sajjad, U., Niu, S., Gurcan, M. N., & Niazi, M. K. K. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3439499 · Cited by 10
Self-citationBreitwieser, M., Zirknitzer, S., Poslusny, K., Freude, T., Scholsching, J., Bodenschatz, K. J., Wagner, A., Hergan, K., Schaffert, M., Metzger, R. P., et al. (2025) · Diagnostics · doi:10.3390/diagnostics15162117 · Cited by 10
Álvarez-Vázquez, J., Casal-Guisande, M., Fernández-García, A., Mosteiro, M., Torres‐Durán, M., & Fernández‐Villar, A. (2025) · Healthcare · doi:10.3390/healthcare13222933 · Cited by 16
Progress in clinical application of artificial intelligence in orthopedics
Wang, Y., Li, R., & Zheng, P. (2022) · Digital Medicine · doi:10.4103/digm.digm_10_21 · Cited by 10
Ward, P., Broadfield, L. A., Dahlberg, P., Leta, G. T., Mekonnen, Z., Nabatte, B., Kabatereine, N. B., Brooks, A., Orrling, K. M., Gomes, M., et al. (2022) · Frontiers in Tropical Diseases · doi:10.3389/fitd.2022.990304 · Cited by 10
Feng, S., Zhang, W., Liu, Y., Chen, S., Lin, M., Feng, M., Yin, J., Tan, L., & Shen, Y. (2023) · Journal of Gastrointestinal Oncology · doi:10.21037/jgo-23-587 · Cited by 10
Harnessing the power of goat milk-derived extracellular vesicles for medical breakthroughs: A review
Meng, Y., Sun, J., & Zhang, G. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.130044 · Cited by 10
Bogaerts, J. M. A., Steenbeek, M. P., Bokhorst, J., van Bommel, M. H. D., Abete, L., Addante, F., Brinkhuis, M., Chrzan, A., Cordier, F., Devouassoux‐Shisheboran, M., et al. (2024) · The Journal of Pathology Clinical Research · doi:10.1002/2056-4538.70006 · Cited by 11
Yang, X., Yang, R., Liu, X., Chen, Z., & Zheng, Q. (2025) · Annals of Surgical Oncology · doi:10.1245/s10434-025-17228-6 · Cited by 11
AI-Based System for Analysis of Electron Microscope Images in Glomerular Disease
Ma, P., Li, J., Zhang, Z., Qiu, W., Li, D., Wang, J., Li, B., Guo, S., Zhang, J., Cen, Z., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.34985 · Cited by 10
Low Tumor-to-Stroma Ratio Reflects Protective Role of Stroma against Prostate Cancer Progression
Nastały, P., Smentoch, J., Popęda, M., Martini, E., Maiuri, P., Zaczek, A. J., Sowa, M., Matuszewski, M., Szade, J., Kalinowski, L., et al. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11111088 · Cited by 10
Legal consciousness and legal culture in the era of total digitalization
Baranov, P. P., Mamychev, A. Y., Дремлюга, Р., & Miroshnichenko, O. I. (2021) · Linguistics and Culture Review · doi:10.21744/lingcure.v5ns3.1665 · Cited by 10
Cao, R., Gu, Q., Tan, D., Wei, P.-J., & Zheng, C.-H. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995576 · Cited by 10
Forecasting Artificial Intelligence Trends in Health Care: Systematic International Patent Analysis
Benjamens, S., Dhunnoo, P., Görög, M., & Meskó, B. (2023) · JMIR AI · doi:10.2196/47283 · Cited by 10
Hemati, S., Kalra, S., Babaie, M., & Tizhoosh, H. R. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107026 · Cited by 10
Kurz, A., Krahl, D., Kutzner, H., Barnhill, R., Perasole, A., Figueras, M. T. F., Ferrara, G., Braun, S. A., Starz, H., Llamas-Velasco, M., et al. (2023) · European Journal of Cancer · doi:10.1016/j.ejca.2023.113294 · Cited by 10
Cheng, J., Liu, Y., Huang, W., Hong, W., Wang, L., Zhan, X., Han, Z., Ni, D., Huang, K., & Zhang, J. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.623382 · Cited by 10
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Vuong, T. T. L., Kim, K., Song, B., & Kwak, J. T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87237-3_52 · Cited by 10
Ma, D., Pasquale, L. R., Girard, M. J. A., Leung, C. K.-S., Jia, Y., Šarunic, M. V., Sappington, R. M., & Chan, K. C. (2023) · Frontiers in Ophthalmology · doi:10.3389/fopht.2022.1057896 · Cited by 10
Challenges and Opportunities for the Veterinary Pathologist in Biomedical Research
Hoenerhoff, M. J., Meyerholz, D. K., Brayton, C. F., & Beck, A. P. (2020) · Veterinary Pathology · doi:10.1177/0300985820974005 · Cited by 9
Jiang, J., Tekin, B., Lin, Y., Armasu, S. M., Winham, S. J., Goode, E. L., Liu, H., Huang, Y., Guo, R., & Wang, C. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.994467 · Cited by 9
Jepsen, D. N. M., Høeg, H., Thagaard, J., Walbech, J. S., Gögenür, I., & Fiehn, A. K. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100152 · Cited by 9
Chinese experts’ consensus on the application of intensive care big data
Su, L., Liu, S., Long, Y., Chen, C., Chen, K., Chen, M., Chen, Y., Cheng, Y., Cui, Y., Ding, Q., et al. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2023.1174429 · Cited by 10
Automated reporting of cervical biopsies using artificial intelligence
Mohammadi, M., Fell, C., MORRISON, D. L., Syed, S., Konanahalli, P., Bell, S., Bryson, G., Arandjelović, O., Harrison, D. J., & Harris‐Birtill, D. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000381 · Cited by 10
Galaz-Montoya, J. G. (2024) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2024.1390858 · Cited by 9
Tracing unknown tumor origins with a biological-pathway-based transformer model
Xie, J., Chen, Y., Luo, S., Yang, W., Lin, Y., Wang, L., Ding, X., Tong, M., & Yu, R. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100797 · Cited by 9
Hard Negative Sample Mining for Whole Slide Image Classification
Huang, W., Hu, X., Abousamra, S., Prasanna, P., & Chen, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72083-3_14 · Cited by 11
Nie, W., Zheng, L., Shen, Y., Zhang, Y., Teng, H., Zhong, R., Cheng, L., Tao, G., Han, B., Chu, T., et al. (2024) · Chinese Medical Journal · doi:10.1097/cm9.0000000000003376 · Cited by 9
Zhang, Y., Xue, Y., Gao, Y., & Zhang, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-80292-7 · Cited by 9
Zhang, X., Han, L., Xu, C., Zheng, Z., Ding, J., Fu, X., Zhang, D., & Han, J. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3513716 · Cited by 9
Ng, C. W., Wong, K., Lawson, B. C., Borgogno, S. F., & Mok, S. C. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-024-06007-8 · Cited by 9
Computer-assisted diagnosis to improve diagnostic pathology: A review
Caputo, A., Maffei, E., Gupta, N., Cima, L., Merolla, F., Cazzaniga, G., Pepe, P., Verze, P., & Fraggetta, F. (2025) · Indian Journal of Pathology and Microbiology · doi:10.4103/ijpm.ijpm_339_24 · Cited by 9
Kang, E., Kim, Y., Kim, Y. C., Kim, E. Y., Lee, N., Kim, Y., Lee, S., Han, S., Choe, M., Hwang, J. H., et al. (2020) · BMC Nephrology · doi:10.1186/s12882-020-02016-z · Cited by 9
Pathological Digital Biomarkers: Validation and Application
Song, Y., Kang, K., Kim, I., & Kim, T. J. (2022) · Applied Sciences · doi:10.3390/app12199823 · Cited by 10
Pan, Y., Sheng, W., Shi, L., Jing, D., Jiang, W., Chen, J., Wang, H., & Qiu, J. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-22-1098 · Cited by 9
Chen, Y., Gao, R., Jing, D., Shi, L., Kuang, F., & Jing, R. (2024) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2024.124030 · Cited by 9
Hatem, N. A. H., Ibrahim, M. I. M., & Yousuf, S. A. (2024) · Scientific Reports · doi:10.1038/s41598-024-80203-w · Cited by 13
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Gupta, R., Le, H., Arnam, J. V., Belinsky, D., Hasan, M., Samaras, D., Kurç, T., & Saltz, J. H. (2020) · Current Pathobiology Reports · doi:10.1007/s40139-020-00217-7 · Cited by 10
Yu, W., Rao, E., Chin, C. D., Aguilar-Jakthong, J., Li, Y., Chow, C., Wang, S. Y., & Rao, J. (2021) · Acta Cytologica · doi:10.1159/000515976 · Cited by 9
Lomacenkova, A., & Arandjelović, O. (2021) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi50953.2021.9508577 · Cited by 9
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Jones‐Hall, Y., Skelton, J. M., & Adams, L. G. (2021) · Journal of Veterinary Medical Education · doi:10.3138/jvme-2021-0068 · Cited by 11
A low-cost pathological image digitalization method based on 5 times magnification scanning
Sun, K. W., Gao, Y., Xie, T., Wang, X., Yang, Q., Chen, L., Wang, K., & Yu, G. (2022) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-21-749 · Cited by 9
Hesso, I., Kayyali, R., Charalambous, A., Lavdaniti, M., Stalika, E., Lelegianni, M., & Nabhani‐Gebara, S. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.888938 · Cited by 9
Cho, H.-G., Cho, S. I., Choi, S., Jung, W., Shin, J., Park, G., Moon, J., Ma, M., Song, H., Mostafavi, M., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12102340 · Cited by 9
The NCI Imaging Data Commons as a platform for reproducible research in computational pathology
Schacherer, D. P., Herrmann, M. D., Clunie, D., Höfener, H., Clifford, W., Longabaugh, W. J. R., Pieper, S., Kikinis, R., Fedorov, A., & Homeyer, A. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107839 · Cited by 11
Predicting primary site of secondary liver cancer with a neural estimator of metastatic origin
Schau, G. F., Burlingame, E. A., Thibault, G., Anekpuritanang, T., Wang, Y., Gray, J. W., Corless, C. L., & Chang, Y. H. (2020) · Journal of medical imaging · doi:10.1117/1.jmi.7.1.012706 · Cited by 9
Sandeep, F., Kiran, N. K., Rahaman, Z., Devi, P., & Bendari, A. (2024) · Cureus · doi:10.7759/cureus.56040 · Cited by 9
The prosubiculum in the human hippocampus: A rostrocaudal, feature‐driven, and systematic approach
Rosenblum, E. W., Williams, E. M., Champion, S. N., Frosch, M. P., & Augustinack, J. C. (2024) · The Journal of Comparative Neurology · doi:10.1002/cne.25604 · Cited by 10
Comes, M. C., Fucci, L., Strippoli, S., Bove, S., Cazzato, G., Colangiuli, C., Risi, I. D., Roma, I. D., Fanizzi, A., Mele, F., et al. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05629-2 · Cited by 8
Digital pathology and artificial intelligence in veterinary medicine
Amaral, C. I., Langohr, I. M., Giaretta, P. R., & Ecco, R. (2024) · Brazilian Journal of Veterinary Pathology · doi:10.24070/bjvp.1983-0246.v17i3p147-151 · Cited by 8
Du, R. F., Carbonell, E. L., Huang, J., Liu, S., Wang, X., Shen, D. G., & Ke, J. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3551913 · Cited by 10
He, W., Wu, X., Jin, Z., Sun, J., Jiang, X., Zhang, S., & Zhang, B. (2025) · Interdisciplinary medicine · doi:10.1002/inmd.20250024 · Cited by 8
Yi, X., Yu, X., Li, C., Li, J., Cao, H., Lu, Q., Li, J., & Hou, J. (2025) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2025.110949 · Cited by 10
Ding, W., Zhang, J.-X., Jin, Z., Hua, H. J., Zu, Q., Yang, S., Wang, W., Liu, S., Zhou, H.-F., & Shi, H.-B. (2025) · Discover Oncology · doi:10.1007/s12672-025-03254-z · Cited by 13
Digital pathology in forensic science: a systematic review of the literature
Ambrosi, L., Amirante, F., Nicolì, S., Marzullo, A., Ingravallo, G., Ferorelli, D., Solarino, B., & Argentiero, G. (2025) · Forensic Science Medicine and Pathology · doi:10.1007/s12024-025-01146-w · Cited by 8
Disentangled Autoencoder for Cross-Stain Feature Extraction in Pathology Image Analysis
Hecht, H., Sarhan, M. H., & Popovici, V. C. (2020) · Applied Sciences · doi:10.3390/app10186427 · Cited by 8
Giovagnoli, M. R., Ciucciarelli, S., Castrichella, L., & Giansanti, D. (2021) · Healthcare · doi:10.3390/healthcare9101347 · Cited by 8
Prognostic factors of breast phyllodes tumors.
Shin, E., & Koo, J. S. (2023) · PubMed · doi:10.14670/hh-18-600 · Cited by 8
Honing, J., & Fitzgerald, R. C. (2023) · Cancer Prevention Research · doi:10.1158/1940-6207.capr-22-0447 · Cited by 8
He, Y., Duan, L., Dong, G., Chen, F., & Li, W. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1345687 · Cited by 8
Liu, Q., Zhang, X., Jiang, X., Zhang, C., Li, J., Zhang, X., Yang, J., Yu, N., Zhu, Y., Liu, J., et al. (2024) · American Journal Of Pathology · doi:10.1016/j.ajpath.2024.01.016 · Cited by 8
Prognostic prediction of gastric cancer based on H&E findings and machine learning pathomics
Han, G., Liu, X., Gao, T., Zhang, L., Zhang, X., Wei, X., Lin, Y., & Yin, B. (2024) · Molecular and Cellular Probes · doi:10.1016/j.mcp.2024.101983 · Cited by 8
Exploring the Potential of Claude 3 Opus in Renal Pathological Diagnosis: Performance Evaluation
Li, X., Liu, K., Lang, Y., Chai, Z., & Liu, F. (2024) · JMIR Medical Informatics · doi:10.2196/65033 · Cited by 7
Whole Slide Imaging Hardware, Software, and Infrastructure
McClintock, D. S., Abel, J. T., & Cornish, T. C. (2021) · Springer eBooks · doi:10.1007/978-3-030-83332-9_2 · Cited by 8
EMPAIA App Interface: An open and vendor-neutral interface for AI applications in pathology
Romberg, D., Strohmenger, K., Jansen, C., Küster, T., Weiss, N., Geißler, C., Sołtysiński, T., Takla, M., Hufnagl, P., Zerbe, N., et al. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106596 · Cited by 8
Tang, X., An, Y., & Li, C. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/2256316 · Cited by 8
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Schukow, C. P., & McKee, P. H. (2023) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2022-0349-ed · Cited by 8
Vizcarra, J. C., Teich, A. F., Dugger, B. N., & Gutman, D. A. (2023) · Free Neuropathology · doi:10.17879/freeneuropathology-2023-4696 · Cited by 8
Fan, Z., Yang, Q., Xia, H., Zhang, P., Sun, K., Yang, M., Yin, R., Zhao, D., Ma, H., Shen, Y., et al. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1066125 · Cited by 8
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Salman, G. A., Kadhim, I. J., & Al-Badri, A. H. (2024) · Journal of Techniques · doi:10.51173/jt.v6i1.1966 · Cited by 9
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Hölscher, D. L., Goedertier, M., Klinkhammer, B. M., Droste, P., Costa, I. G., Boor, P., & Bülow, R. D. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05721-w · Cited by 7
Zhao, L., Zhao, W., Qiu, L., Jiang, M., Qian, L., Ting, H.-N., Fu, X., Zhang, P., Han, Y., & Zhao, J. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107097 · Cited by 7
Shen, M., & Jiang, Z. (2024) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s485724 · Cited by 7
Wan, W., Liu, H., Zou, J., Xie, T., Zhang, G., Ying, W., & Zou, X. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2024.1503404 · Cited by 7
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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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