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
13 ABSTRACT: The combination of mass spectrometry imaging 14 and histology has proven a powerful app...
Mass spectrometry imaging (MSI) is a powerful molecular imaging technique. In microprobe MSI, images...
Mass spectrometry imaging (MSI) is a powerful molecular imaging technique. In microprobe MSI, images...
Dimensionality reduction approaches are commonly used for the deconvolution of high-dimensional meta...
Extracting pathology information embedded within surface optical properties in Spatial Frequency Dom...
Variational autoencoders (VAEs) are deep latent space generative models that have been immensely suc...
Mass Spectrometry Imaging (MSI) is a sensitive analytical tool for detecting and spatially localisin...
The goal of mass spectrometry-based imaging (MSI) is to characterize the chemical composition of bio...
Supervised modeling of mass spectrometry imaging (MSI) data is a crucial component for the detection...
International audienceInterpretable modeling of heterogeneous data channels is essential in medical ...
Research on metabolic heterogeneity provides an important basis for the study of the molecular mecha...
Personalized treatment methods for a complex disease such as cancer benefit from using multiple data...
Clustering high-dimensional data, such as images or biological measurements, is a long-standing prob...
AbstractExploration of tissue sections by imaging mass spectrometry reveals abundance of different b...
Clustering high-dimensional data, such as images or biological measurements, is a long-standing prob...
13 ABSTRACT: The combination of mass spectrometry imaging 14 and histology has proven a powerful app...
Mass spectrometry imaging (MSI) is a powerful molecular imaging technique. In microprobe MSI, images...
Mass spectrometry imaging (MSI) is a powerful molecular imaging technique. In microprobe MSI, images...
Dimensionality reduction approaches are commonly used for the deconvolution of high-dimensional meta...
Extracting pathology information embedded within surface optical properties in Spatial Frequency Dom...
Variational autoencoders (VAEs) are deep latent space generative models that have been immensely suc...
Mass Spectrometry Imaging (MSI) is a sensitive analytical tool for detecting and spatially localisin...
The goal of mass spectrometry-based imaging (MSI) is to characterize the chemical composition of bio...
Supervised modeling of mass spectrometry imaging (MSI) data is a crucial component for the detection...
International audienceInterpretable modeling of heterogeneous data channels is essential in medical ...
Research on metabolic heterogeneity provides an important basis for the study of the molecular mecha...
Personalized treatment methods for a complex disease such as cancer benefit from using multiple data...
Clustering high-dimensional data, such as images or biological measurements, is a long-standing prob...
AbstractExploration of tissue sections by imaging mass spectrometry reveals abundance of different b...
Clustering high-dimensional data, such as images or biological measurements, is a long-standing prob...
13 ABSTRACT: The combination of mass spectrometry imaging 14 and histology has proven a powerful app...
Mass spectrometry imaging (MSI) is a powerful molecular imaging technique. In microprobe MSI, images...
Mass spectrometry imaging (MSI) is a powerful molecular imaging technique. In microprobe MSI, images...