NIST · ИТ · 931b37dccd4558191383
Impact of Sampling and Augmentation on Estimated Accuracy of Microscopy Image Segmentation Methods
Sampling and augmentation methods have been widely used to generate sufficiently large numbers of representative training images for supervised segmentation methods, such as convolutional neural networks (CNN). There is a need to understand the impact of
NIST · ИТОфициальный источник · 2018-06-22
{
"title_en": "Impact of Sampling and Augmentation on Estimated Accuracy of Microscopy Image Segmentation Methods",
"designation": "",
"series": "",
"status": "",
"date": "2018-06-22",
"date_original": "June 22, 2018",
"url": "https://www.nist.gov/publications/impact-sampling-and-augmentation-estimated-accuracy-microscopy-image-segmentation",
"source": "NIST Publications",
"display_en": "",
"authors_en": "Michael P. Majurski , Petru S. Manescu , Joe Chalfoun , Peter Bajcsy , Mary C. Brady",
"abstract_excerpt_en": "Sampling and augmentation methods have been widely used to generate sufficiently large numbers of representative training images for supervised segmentation methods, such as convolutional neural networks (CNN). There is a need to understand the impact of",
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{
"title_en": "Impact of Sampling and Augmentation on Estimated Accuracy of Microscopy Image Segmentation Methods",
"designation": "",
"series": "",
"status": "",
"date": "2018-06-22",
"date_original": "June 22, 2018",
"url": "https://www.nist.gov/publications/impact-sampling-and-augmentation-estimated-accuracy-microscopy-image-segmentation",
"source": "NIST Publications",
"display_en": "",
"authors_en": "Michael P. Majurski , Petru S. Manescu , Joe Chalfoun , Peter Bajcsy , Mary C. Brady",
"abstract_excerpt_en": "Sampling and augmentation methods have been widely used to generate sufficiently large numbers of representative training images for supervised segmentation methods, such as convolutional neural networks (CNN). There is a need to understand the impact of"
}
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