SVM classification and data augmentation methodology.

<p>20×20×20 patches of NMF data sampled from different regions of labeled CPH and naïve datasets were used to train and test the SVM classifier.</p>

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Bibliographic Details
Main Author:	Kasun Pathirage (19832012) (author)
Other Authors:	Aman Virmani (19832015) (author), Alison J. Scott (9428011) (author), Richard J. Traub (12553162) (author), Robert K. Ernst (127237) (author), Reza Ghodssi (705823) (author), Behtash Babadi (5156105) (author), Pamela Ann Abshire (19832018) (author)
Published:	2024
Subjects:	Biotechnology Immunology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified powerful scientific tool negative matrix factorization large file sizes interpretable dimensionality reduction additional clustering steps second challenge unaddressed readily interpretable features different tissue types stained tissue images msi data processing components may also 20 &# 215 msi data analysis spectral components generate images first challenge features according data augmentation &# 8764 tissue structures tissue structure enhance tissue msi datasets msi dataset yielding spatial three orders support training spatial distribution small patches resolve two nmf data necessarily result meaningful correlation image registration female sprague enhanced specificity dawley rats cph ). correlate well control subjects biochemical compounds biggest challenges animal model 20 pixels
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