Blind hyperspectral unmixing of pump-probe images with maximum likelihood selection of a convolutional network from an ensemble of models

Arya C. Mugdha; Jesse W. Wilson

doi:10.1117/12.2668708

15 March 2023 Blind hyperspectral unmixing of pump-probe images with maximum likelihood selection of a convolutional network from an ensemble of models

Arya C. Mugdha, Jesse W. Wilson

Author Affiliations +

Proceedings Volume 12392, Advanced Chemical Microscopy for Life Science and Translational Medicine 2023; 123920C (2023) https://doi.org/10.1117/12.2668708
Event: SPIE BiOS, 2023, San Francisco, California, United States

Conference Poster

Abstract

We describe a simple convolutional network for blind unmixing of transient absorption microscopy data along with a model ensembling strategy. Our network is based on an autoencoder previously developed for blind unmixing of hyperspectral satellite images. Its advantages are (a) that it learns to unmix spectra by unsupervised learning, i.e. by learning to reconstruct imaging data, without knowledge of the underlying spectra or their abundances and (b) that the endmember spectra are directly encoded by the output layer’s coefficients. Extensive modifications to both the network architecture and training loss functions were necessary to produce reasonable performance on transient absorption data. We demonstrate results from blind unmixing of transient absorption images of unstained muscle fibers, acquired at 520 nm pump and 620 nm probe, training an ensemble of 500 different networks (i.e. unmixing models), each starting from a different random initialization. Variability among resulting models was analyzed by principal component analysis on the recovered endmembers from all models, deriving from the projections a model probability density function. We found consistent models (predicting similar endmembers and abundance maps) near the most likely model and surrounding high-probability region, with more variability in low-probability regions. Then, a permutation-aligned average of the ensemble produced much better results than an unweighted ensemble average, or simple selection of one model based on maximum likelihood or best fit. We anticipate this approach of parametrizing models and ensembling based on relative probability to have applications in other chemical imaging modalities such as FLIM, Raman microscopy and mass spectroscopic imaging.

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Arya C. Mugdha and Jesse W. Wilson "Blind hyperspectral unmixing of pump-probe images with maximum likelihood selection of a convolutional network from an ensemble of models", Proc. SPIE 12392, Advanced Chemical Microscopy for Life Science and Translational Medicine 2023, 123920C (15 March 2023); https://doi.org/10.1117/12.2668708

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