Variational autoencoders for tissue heterogeneity exploration from (almost) no preprocessed mass spectrometry imaging data
arXiv:1708.07012v2
Originality Synthesis-oriented
AI Analysis
This addresses tissue heterogeneity analysis for biomedical researchers, but appears incremental as it applies an existing method to a specific domain.
The paper applied Variational Autoencoders to reduce dimensionality and explore mass spectrometry imaging data, showing they detect tissue sub-type patterns with better performance than standard approaches.
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.