Fig. 1. Configuration of two types of transmission Mueller matrix microscopes^[12]. (a) Dual rotating retarders-based Mueller matrix microscope; (b) dual division of focal plane polarimeters-based Mueller matrix microscope

Fig. 2. Images of polarization parameters of breast cancer tissues^[38]. (a) Parameters D_L, t₁, Δ, D, and b correspond to the cell nuclei (labeled by the black solid line in the H&E stained image); (b) parameters r_L, q_L, δ, P_L,_{and θ correspond to the fiber tissue (marked by the red solid line in the H&E stained image)}

Fig. 3. Dual-modality machine learning framework for pixel level polarization feature extraction of cervical precancerous tissues^[47]. (a) Mueller matrix light intensity image m11 and H&E image registration; (b) U-Net segmentation of cervical epithelial region in H&E image to generate mask M; (c) mapping M to PBPs to select target pixels; (d) derive a PFP by inputting target pixels in to a statistical distance-based machine learning model; (e) using PFP to distinguish normal and 3 stages of cervical precancerous lesions

Fig. 4. Polarization parameter images based radiomics approach^[43]

Fig. 5. Pixel clustering of Mueller matrix images from 222 ROIs of liver cancer tissues^[52]

Fig. 6. Identification of polarization markers that correlates with hepatocellular carcinoma (HCC) differentiation degree^[52]. (a) Density heatmap of normal and malignant HCC, zoomed in on two potential polarization markers, cluster 2 and 6; (b) proportion of pixels belongs to cluster 2 in each ROI are calculated for each differentiation degree; (c) proportion of pixels belongs to cluster 6 in each ROI are calculated for each differentiation degree; (d) density heatmap for each differentiation degree, the dashed line indicates the selected polarization marker in the cytoplasm cluster to differentiate well and moderately differentiated samples; (e) in well and moderately differentiated samples, calculate the pixel proportion of the selected cytoplasm cluster

Fig. 7. Polarization super-pixels based label extension process for lung cancer ROIs. (a) Pathologist provides a small initial label within the red box; (b) obtain extended label for the whole red box by selecting super-pixels with high contribution, pathologist identify and retain the correct label; (c) expand the red box, pathologist repeat polarization super-pixels calculation and selection to obtain extended label; (d) by iterating the above process, the entire extended label for the ROI is obtained; (e) extend the label to a new ROI 1; (f) extend the label to a new ROI 2