Bayesian neural networks enabled snapshot polarized light scattering spectroscopy with uncertainty quantification

Polarized light-scattering spectroscopy (PLSS) is a promising noninvasive early cancer detection technique via inversing the nuclear size of epithelial cells from collected single scattered light. Conventionally, the nuclear size is inferred by using model-driven least square fitting (LSF) methods, which is time-consuming and challenging for real-time cancer diagnosis. Although data-driven deep-learning-based techniques can be employed to speed up the inverse process, the labeled data are usually limited and the reliability quantification of prediction is lacking. Herein, by synthesizing a large quantity of single scattered spectra with physical priori, a Bayesian-deep-learning (BDL)-based PLSS framework is presented for the first time as far as we know, and is expected to provide model uncertainty of network prediction for the reliability quantification. Further, to explore the applicability of the BDL uncertain estimation, the neural networks for early cancer diagnosis are designed as a convolutional neural network (CNN) classification problem and a fully connected neural network (FNN) regression problem, respectively. They are verified using the single scattered spectra of polystyrene microsphere tissue models and human colorectal lesion tissue obtained by our proposed snapshot PLSS system. The results show that both BDL networks obtain similar nuclear sizes to the ground truth and their inferring speeds are far faster than LSF methods. Further, it is interesting to note that only the CNN classification network can provide effective uncertainty for reliability quantification. The work presents a new paradigm for the real-time diagnosis of early cancer and provides a reference for the design of snapshot PLSS endoscopy.