Cited by 544AI in cancer detectionArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

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Artificial intelligence and computational pathology

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Cui, Miao, Zhang, David Y.·2021·Laboratory Investigation

DOI 10.1038/s41374-020-00514-0PMC PMC7811340

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Abstract

Data processing and learning has become a spearhead for the advancement of medicine, with pathology and laboratory medicine has no exception. The incorporation of scientific research through clinical informatics, including genomics, proteomics, bioinformatics, and biostatistics, into clinical practice unlocks innovative approaches for patient care. Computational pathology is burgeoning subspecialty in pathology that promises a better-integrated solution to whole-slide images, multi-omics data, and clinical informatics. However, computational pathology faces several challenges, including the ability to integrate raw data from different sources, limitation of hardware processing capacity, and a lack of specific training programs, as well as issues on ethics and larger societal acceptable practices that are still solidifying. The establishment of the entire industry of computational pathology requires far-reaching changes of the three essential elements connecting patients and doctors: the local laboratory, the scan center, and the central cloud hub/portal for data processing and retrieval. Computational pathology, unlocked through information integration and advanced digital communication networks, has the potential to improve clinical workflow efficiency, diagnostic quality, and ultimately create personalized diagnosis and treatment plans for patients. This review describes clinical perspectives and discusses the statistical methods, clinical applications, potential obstacles, and future directions of computational pathology.

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Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., & Fuchs, T. J. (2019) · Nature Medicine · doi:10.1038/s41591-019-0508-1 · Cited by 2,799
Opportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310
Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-019-0252-y · Cited by 1,698
Digital pathology and artificial intelligence
Niazi, M. K. K., Parwani, A. V., & Gurcan, M. N. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30154-8 · Cited by 1,232
Image analysis and machine learning in digital pathology: challenges and opportunities
Madabhushi, A., & Lee, G. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.06.037 · Cited by 1,093
Predicting cancer outcomes from histology and genomics using convolutional networks
Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D. A., Barnholtz-Sloan, J. S., Velázquez Vega, J. E., Brat, D. J., & Cooper, L. A. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717139115 · Cited by 1,084
Deep Learning for Identifying Metastatic Breast CancerPreprint
Wang, D., Khosla, A., Gargeya, R., Irshad, H., & Beck, A. H. (2016) · arXiv.org · doi:10.48550/arxiv.1606.05718 · Cited by 1,028
Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images
Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.086 · Cited by 1,027
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
Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification
Hou, L., Samaras, D., Kurç, T., Gao, Y., Davis, J., & Saltz, J. H. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.266 · Cited by 921

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Naseri, S., Shukla, S., Hiwale, K., Jagtap, M. M., Gadkari, P. V., Gupta, K., Deshmukh, M., & Sagar, S. (2024) · Cureus · doi:10.7759/cureus.59171 · Cited by 8
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Hsu, C.-H., Yeh, C.-F., Huang, I.-S., Chen, W.-J., Peng, Y., Tsai, C.-H., Ko, M.-C., Su, C.-P., Chen, H.-C., Wu, W., et al. (2024) · Journal of Assisted Reproduction and Genetics · doi:10.1007/s10815-024-03215-5 · Cited by 4
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Loh, E., & Nguyen, T. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-821750-4.00002-5 · Cited by 5
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Authors: Cui, M., & Zhang, D. Y.
Affiliations: St. Luke's-Roosevelt Hospital Center (US)
Veterans Health Administration (US)
Corresponding author: Zhang, David Y.
ORCID: Cui, Miao
Zhang, David Y.
Venue: Laboratory Investigation
Publisher: Elsevier BV
Published: 2021-04
Volume: 101
Issue: 4
Pages: 412-422
ISSN: 0023-6837 (ISSN-L)
DOI: 10.1038/s41374-020-00514-0
PMC: PMC7811340
OpenAlex: W3121943121
Cited by: 544
References: 64
Citations by year: 2026: 68 · 2025: 103 · 2024: 126 · 2023: 127 · 2022: 97 · 2021: 23
FWCI: 6.74
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 650 d)
Locations: Laboratory Investigation (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
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: computational pathology, artificial intelligence in pathology, whole slide images, multi-omics integration, clinical informatics, cloud processing, clinical workflow efficiency, personalized diagnosis, AI ethics
MeSH terms: Artificial Intelligence, Humans, Medical Informatics, Pathology, Clinical, Statistics as Topic, Computational Biology

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