A Versatile Deep Learning Architecture for Classification and Label-Free Prediction of Hyperspectral Images.

ABSTRACT: Hyperspectral imaging is a technique that provides rich chemical or compositional information not regularly available to traditional imaging modalities such as intensity imaging or color imaging based on the reflection, transmission, or emission of light. Analysis of hyperspectral imaging often relies on machine learning methods to extract information. Here, we present a new flexible architecture, the U-within-U-Net, that can perform classification, segmentation, and prediction of orthogonal imaging modalities on a variety of hyperspectral imaging techniques. Specifically, we demonstrate feature segmentation and classification on the Indian Pines hyperspectral dataset and simultaneous location prediction of multiple drugs in mass spectrometry imaging of rat liver tissue. We further demonst

SUBMITTER: Manifold B 

PROVIDER: S-EPMC8528004 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature