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Seamless lesion insertion for data augmentation in CAD training

The performance of a classifier is largely dependent on the size and representativeness of data used for its training. In circumstances where accumulation and/or labeling of training samples is difficult or expensive, such as medical applications, data augmentation can potentially be used to allevia...

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Vydáno v:IEEE Trans Med Imaging
Hlavní autoři: Pezeshk, Aria, Petrick, Nicholas, Chen, Weijie, Sahiner, Berkman
Médium: Artigo
Jazyk:Inglês
Vydáno: 2016
Témata:
On-line přístup:https://ncbi.nlm.nih.gov/pmc/articles/PMC5509514/
https://ncbi.nlm.nih.gov/pubmed/28113310
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/TMI.2016.2640180
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