Variational autoencoders for tissue heterogeneity exploration from
(almost) no preprocessed mass spectrometry imaging data
(almost) no preprocessed mass spectrometry imaging data
File(s)1708.07012v2.pdf (3.12 MB)
Published version
Author(s)
Inglese, Paolo
Alexander, James L
Mroz, Anna
Takats, Zoltan
Glen, Robert
Type
Journal Article
Abstract
The paper presents the application of Variational Autoencoders (VAE) for data
dimensionality reduction and explorative analysis of mass spectrometry imaging
data (MSI). The results confirm that VAEs are capable of detecting the patterns
associated with the different tissue sub-types with performance than standard
approaches.
dimensionality reduction and explorative analysis of mass spectrometry imaging
data (MSI). The results confirm that VAEs are capable of detecting the patterns
associated with the different tissue sub-types with performance than standard
approaches.
Date Acceptance
2017-08-24
Citation
arXiv
Journal / Book Title
arXiv
Copyright Statement
© The authors
Identifier
http://arxiv.org/abs/1708.07012v2
Subjects
q-bio.QM
q-bio.QM
cs.LG
stat.ML
Notes
mass spectrometry imaging, variational autoencoder, desorption electrospray ionization, desi