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Found 3 papers, 1 papers with code
Deep Learning-Based Correction and Unmixing of Hyperspectral Images for Brain Tumor Surgery
The models were evaluated against phantom and pig brain data with known PpIX concentration; the supervised model achieved Pearson correlation coefficients (R values) between the known and computed PpIX concentrations of 0. 997 and 0. 990, respectively, whereas the classical approach achieved only 0. 93 and 0. 82.
A Spectral Library and Method for Sparse Unmixing of Hyperspectral Images in Fluorescence Guided Resection of Brain Tumors
With this knowledge, we introduce (1) a library of 9 endmember spectra, (2) a sparse, non-negative Poisson regression algorithm to perform physics-informed unmixing with this library without overfitting, and (3) a highly realistic spectral measurement simulation with known endmember abundances.
Towards Machine Learning-based Quantitative Hyperspectral Image Guidance for Brain Tumor Resection
For tissue type, at least four of the five fluorophore abundances were found to be significantly different (p < 0. 01) between all classes.
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