Neural networks, a subclass of methods in the broader field of machine learning, are highly effective in enabling computer systems to analyze data, facilitating the work of clinicians. Neural networks have been used since the 1980s, with convolutional neural networks (CNNs) applied to images beginning in the 1990s.1-3 Examples include identifying natural images of everyday life,4 classifying retinal pathology,5 selecting cellular elements on pathological slides,6 and correctly identifying the spatial orientation of chest radiographs.7 Successful neural networks for such tasks are typically composed of multiple analysis layers; the term deep learning is also (synonymously) used to describe this class of neural networks.
Carin L, Pencina MJ. On Deep Learning for Medical Image Analysis. JAMA. 2018;320(11):1192–1193. doi:10.1001/jama.2018.13316
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