Details:

Alternative Title:
IEEE Int Symp Technol Homel Security HST
Personal Author:
Jurrus, Elizabeth ; Hodas, Nathan ; Baker, Nathan ; Marrinan, Tim ; Hoover, Mark D.
Jurrus, Elizabeth ; Hodas, Nathan ; Baker, Nathan ; Marrinan, Tim ; Hoover, Mark D. Less -
Description:
Image classification of nanoparticles from scanning electron microscopes for nuclear forensic analysis is a long, time consuming process. Months of analyst time may initially be required to sift through images in order to categorize morphological characteristics associated with nanoparticle identification. Subsequent assessment of newly acquired images against identified characteristics can be equally time consuming. We present INStINCt, our Intelligent Signature Canvas, as a framework for quickly organizing image data in a web-based canvas framework that partitions images based on features derived from convolutional neural networks. This work is demonstrated using particle images from an aerosol study conducted by Pacific Northwest National Laboratory under the auspices of the U.S. Army Public Health Command to determine depleted uranium aerosol doses and risks.
Subjects:
[+]
Article
Source:
IEEE Int Symp Technol Homel Security HST. 2016.
Pubmed ID:
30191203
Pubmed Central ID:
PMC6121815
Document Type:
Journal Article
Funding:
CC999999/Intramural CDC HHS/United States
Collection(s):
CDC Public Access
Main Document Checksum:
[+]
urn:sha256:9e24afd883b65032126990791eff0fcd796b6e143104387ce40fd8db1491da30
Download URL:
https://stacks.cdc.gov/view/cdc/58858/cdc_58858_DS1.pdf
File Type:

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