Zobrazeno 1 - 2
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pro vyhledávání: '"Alexander J, Ratner"'
Autor:
Jared A. Dunnmon, Alexander J. Ratner, Khaled Saab, Nishith Khandwala, Matthew Markert, Hersh Sagreiya, Roger Goldman, Christopher Lee-Messer, Matthew P. Lungren, Daniel L. Rubin, Christopher Ré
Publikováno v:
Patterns, Vol 1, Iss 2, Pp 100019- (2020)
Summary: A major bottleneck in developing clinically impactful machine learning models is a lack of labeled training data for model supervision. Thus, medical researchers increasingly turn to weaker, noisier sources of supervision, such as leveraging
Externí odkaz:
https://doaj.org/article/078253786f68414aa2f86514760d3eac
Publikováno v:
Advances in neural information processing systems. 30
Data augmentation is a ubiquitous technique for increasing the size of labeled training sets by leveraging task-specific data transformations that preserve class labels. While it is often easy for domain experts to specify individual transformations,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::cec793d3fbd40e346e73e3ef12c327d5
https://pubmed.ncbi.nlm.nih.gov/29375240
https://pubmed.ncbi.nlm.nih.gov/29375240