On deep learning for medical image analysis

Lawrence Carin, Michael J. Pencina

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Neural networks, a subclass of methods in the broader field of machine learning, are highly effective in enabling computer systems to analyze data, facilitating thework of clinicians. Neuralnetworks have beenusedsincethe1980s,withconvolutionalneuralnetworks(CNNs) applied to images beginning in the 1990s.1-3 Examples include identifying natural images of everyday life,4 classifying retinal pathology,5 selectingcellular elements on pathological slides,6 andcorrectly identifyingthe spatial orientation ofchest 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.
Original languageEnglish (US)
Pages (from-to)1192-1193
Number of pages2
JournalJAMA - Journal of the American Medical Association
Volume320
Issue number11
DOIs
StatePublished - Sep 18 2018
Externally publishedYes

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