Polarization images encode high resolution microstructural information even at low resolution. We propose a framework combining polarization imaging and traditional microscopy imaging, constructing a dualmodality machine learning framework that is not only accurate but also generalizable and interpretable. We demonstrate the viability of our proposed framework using the cervical intraepithelial neoplasia grading task, providing a polarimetry feature parameter to quantitatively characterize microstructural variations with lesion progression in hematoxylin-eosin-stained pathological sections of cervical precancerous tissues. By taking advantages of polarization imaging techniques and machine learning methods, the model enables interpretable and quantitative diagnosis of cervical precancerous lesion cases with improved sensitivity and accuracy in a lowresolution and wide-field system. The proposed framework applies routine image-analysis technology to identify the macro-structure and segment the target region in H&Estained pathological images, and then employs emerging polarization method to extract the micro-structure information of the target region, which intends to expand the boundary of the current image-heavy digital pathology, bringing new possibilities for quantitative medical diagnosis.

No abstract

A Mueller matrix is a comprehensive representation of the polarization transformation properties of a sample, encoding very rich information on the microstructure of the scattering objects. However, it is often inconvenient to use individual Mueller matrix elements to characterize the microstructure due to a lack of explicit connections between the matrix elements and the physics properties of the scattering samples. In this review, we summarize the methods to derive groups of polarization parameters, which have clear physical meanings and associations with certain structural properties of turbid media, including various Mueller matrix decomposition (MMD) methods and the Mueller matrix transformation (MMT) technique. Previously, experimentalists have chosen the most suitable method for the specific measurement scheme. In this review, we introduce an emerging novel research paradigm called 'polaromics'. In this paradigm, both MMD and MMT parameters are considered as polarimetry basis parameters (PBP), which are used to construct polarimetry feature parameters (PFPs) for the quantitative characterization of complex biomedical samples. Machine learning techniques are involved to find PFPs that are sensitive to specific micro-or macrostructural features. The goal of this review is to provide an overview of the emerging 'polaromics' paradigm, which may pave the way for biomedical and clinical applications of polarimetry.

Porous anodic alumina (PAA) is a photonic crystal with a hexagonal porous structure. To learn more about the effects brought by pores on the anisotropy of the PAA, we use the orientation sensitive Mueller matrix imaging (MMI) method to study it. We fabricated the PAA samples with uniform pores and two different pore diameters. By the MMI experiments with these samples, we found that the birefringence is the major anisotropy of the PAA and that there are many small areas with different orientations that formed spontaneously in the process of production on the surface of the PAA. By the MMI experiments at different orientations of the sample with two different pore diameters, we found that the pores affect the birefringence of the sample and the effect increases with the increased inclination of the sample. To further analyze the PAA, we present a symmetrical rotation measurement method according to the Mueller matrix of the retarder. With this method, we can calculate the average refractive index (RI) of birefringence and the orientation of the optical axis of uniaxial crystal. The results also show the effect of the pores on the anisotropy of PAA.

We propose a polarization-based probabilistic discriminative model for deriving a set of new sigmoid-transformed polarimetry feature parameters, which not only enables accurate and quantitative characterization of cancer cells at pixel level, but also accomplish the task with a simple and stable model. By taking advantages of polarization imaging techniques, these parameters enable a low-magnification and wide-field imaging system to separate the types of cells into more specific categories that previously were distinctive under high magnification. Instead of blindly choosing the model, the L0 regularization method is used to obtain the simplified and stable polarimetry feature parameter. We demonstrate the model viability by using the pathological tissues of breast cancer and liver cancer, in each of which there are two derived parameters that can characterize the cells and cancer cells respectively with satisfactory accuracy and sensitivity. The stability of the final model opens the possibility for physical interpretation and analysis. This technique may bypass the typically labor-intensive and subjective tumor evaluating system, and could be used as a blueprint for an objective and automated procedure for cancer cell screening.