The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods
Open accessAbstract
Increased interest in the opportunities provided by artificial intelligence and machine learning has spawned a new field of health-care research. The new tools under development are targeting many aspects of medical practice, including changes to the practice of pathology and laboratory medicine. Optimal design in these powerful tools requires cross-disciplinary literacy, including basic knowledge and understanding of critical concepts that have traditionally been unfamiliar to pathologists and laboratorians. This review provides definitions and basic knowledge of machine learning categories (supervised, unsupervised, and reinforcement learning), introduces the underlying concept of the bias-variance trade-off as an important foundation in supervised machine learning, and discusses approaches to the supervised machine learning study design along with an overview and description of common supervised machine learning algorithms (linear regression, logistic regression, Naive Bayes, k-nearest neighbor, support vector machine, random forest, convolutional neural networks).
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Using machine learning approaches for multi-omics data analysis: A review
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Artificial intelligence and computational pathology
Cui, M., & Zhang, D. Y. (2021) · Laboratory Investigation · doi:10.1038/s41374-020-00514-0 · Cited by 544
Early lung cancer diagnostic biomarker discovery by machine learning methods
Xie, Y., Meng, W.-Y., Li, R.-Z., Wang, Y.-W., Qian, X., Chan, C., Yu, Z.-F., Fan, X.-X., Pan, H.-D., Xie, C., et al. (2021) · Translational Oncology · doi:10.1016/j.tranon.2020.100907 · Cited by 269
Independent real‐world application of a clinical‐grade automated prostate cancer detection system
da Silva, L. M., Pereira, E. M., Salles, P. G., Godrich, R., Ceballos, R., Kunz, J. D., Casson, A., Viret, J., Chandarlapaty, S., Ferreira, C. G., et al. (2021) · The Journal of Pathology · doi:10.1002/path.5662 · Cited by 175
Albahra, S., Gorbett, T., Robertson, S., D'Aleo, G., Kumar, S. V. S., Ockunzzi, S., Lallo, D., Hu, B., & Rashidi, H. H. (2023) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2023.02.002 · Cited by 139
Self-citation
Metadata
- Authors
- Rashidi, H. H., Tran, N. K., Betts, E. V., Howell, L. P., & Green, R.
- Venue
- Academic Pathology
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 6
- Pages
- 2374289519873088
- ISSN
- 2374-2895 (ISSN-L)
- OpenAlex
- W2971361125
- Cited by
- 371
- References
- 45
- FWCI
- 18.76
- Type
- journal article
- Language
- en
- Open access
- gold
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- Rashidi, H. H., Tran, N. K., Betts, E. V., Howell, L. P., & Green, R. (2019). Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods. Academic Pathology. https://doi.org/10.1177/2374289519873088
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