Compressive Raman classification of cells using a neural network for optical filter design and cell classification
Abstract
[0000] A method for cell classification performs a compressive Raman measurement of a cell sample that involves frequency filtering the dispersed optical signal by a tunable optical filter whose frequency response is defined by weights that were derived from trained weights of a first hidden layer of a neural network trained on Raman spectra of cells and corresponding labels. The frequency filtered signals are detected by an optical detector to produce a compressive Raman measurement. Multiple compressive Raman measurements are then input to a prediction neural network to predict a label of the cell sample. The prediction neural network is the same as the trained neural network except without the input layer and the weights of the first hidden layer.
Inventors 1
- Stefan Semrau Aachen author as filed: Semrau, Stefan
Inventors are linked to an Aachen author when surname and first name match exactly one author.
Applicants
- Universiteit Leiden GBS Leiden
- 莱顿大学
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