Automatic detection of head and neck squamous cell carcinoma on pathologic slides using polarized hyperspectral imaging and machine learning

The aim of this study is to incorporate polarized hyperspectral imaging (PHSI) with machine learning for automatic detection of head and neck cancer on H&E stained tissue slides. A polarized hyperspectral imaging microscope had been developed in our group. The preliminary results showed that the spectral curves of the Stokes vector parameters (S0, S1, S2, S3) on the H&E stained tissue slides with squamous cell carcinoma (SCC) from the larynx are different from normal tissue in certain wavelength range. In this paper, we imaged 20 H&E stained tissue slides from 10 patients with SCC of the larynx by the PHSI microscope. Several machine learning algorithms (support vector machine, random forest, Gaussian naive Bayes, logistic regression) were applied to the collected image data for the automatic detection of SCC on the H&E stained tissue slides. The performance of these methods was compared among the collected polarized hyperspectral imaging dataset of tissue slides, the pseudo RGB images generated from the polarized hyperspectral imaging dataset, and the principle component analysis (PCA) transformation of the polarized hyperspectral imaging dataset. The results suggest that SVM is a superior classifier for the classification task based on polarized hyperspectral data cubes compared to the other three types of classifiers. Furthermore, the incorporation of the four Stokes vector parameters improved the classification accuracy. Finally, the PCA transformed image data did not improve the accuracy as it might lose some important information from the original polarized hyperspectral data cube. Polarized hyperspectral imaging can have many potential applications in digital pathology.