Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods
By Hooman H. Rashidi, Nam K. Tran, E. Betts, L. Howell, Ralph Green. Academic Pathology, 2019
306 citations recorded by the source corpus.
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).
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/3f22c9462f8e588ce4210a304133e2265f41d913
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In Artificial Intelligence
In Computer Vision and Pattern Recognition
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging (2023, methodology): “Central to supervised learning are algorithms such as linear regression, logistic regression, and decision trees [36].”