Mr. Xingjian Wang Profile

Xingjian Wang

at Tsinghua Univ

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Proceedings Article | 4 March 2022 Presentation + Paper

Deep learning-based polarization feature retrieval from a single Stokes vector

Lu Si, Tongyu Huang, Xingjian Wang, Yue Yao, Hui Ma

Proceedings Volume 11963, 1196307 (2022) https://doi.org/10.1117/12.2609583

KEYWORDS: Polarization, Polarimetry, Tissues, Liver, Blood, Microscopes, Feature extraction, Polarizers

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Polarization is capable of probing microstructures and has unique sensitivity to fibrous anisotropic structure. Polarimetric imaging has demonstrated promising potential in diverse applications ranging from biomedicine, material science, and atmospheric remote sensing. The polarization properties of samples can be comprehensively described by a Mueller matrix (MM). However, the relationship between individual MM elements and properties of the sample is often not clear. There have been consistent efforts to derive polarization parameters from MM based on certain assumptions for better description of the samples, e.g., MM polar decomposition (MMPD), MM transformation (MMT) and MM differential decomposition. Usually, the MM imaging requires sequential measurements with different polarization states of incident light and the imaging process is time consuming. In addition, for movable samples, we cannot guarantee the consistency during the imaging. This may cause precision issues since the images cannot be well-registered. In this work, we built a statistical translation model to generate polarization parameters from a single Stokes vector which can be obtained by one-shot imaging. This will improve the imaging efficiency, simplify the optical system and avoid introducing errors by the image registration. In the model design, we adopted the generative adversarial network (GAN) where the generator is based on a U-net architecture. We demonstrated the effectiveness of our approach on liver tissue, blood smear and porous anodic alumina (PAA) film, and quantitatively evaluated the results by similarity assessment methods. The model can generate a parameter image within 0.1 second on a desktop computer, which shows the potential to achieve real-time performance.

Proceedings Article | 10 October 2020 Poster + Paper

Quantitatively distinguishing typical pathological features between different breast tissues using polarimetry feature parameters

Yang Dong, Anli Hou, Xingjian Wang, Hui Ma

Proceedings Volume 11553, 115532N (2020) https://doi.org/10.1117/12.2575162

KEYWORDS: Breast, Tissues, Polarimetry, Collagen, Mammography, Feature extraction, Algorithm development, Statistical analysis

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Breast diseases with many distinct histopathological types are showing a rising trend in incidence for decades worldwide. The proliferation of cells and the remodeling of collagen fibers in breast carcinoma tissues may be used to predict breast disease diagnosis, prognosis of treatment, and patient survival. Pathologists can label related typical pathological features as cell nuclei, aligned collagen, and disorganized collagen in hematoxylin and eosin (HE) sections of breast tissues. In this study, we apply the Mueller matrix microscopic imaging to various breast pathological section samples, and calculate corresponding polarimetry basis parameters (PBPs). A pixel-based extraction approach of polarimetry feature parameters (PFPs) is proposed using a mutual information (MI) method and a linear discriminant analysis (LDA) classifier. The three PFPs derived by the proposed learning algorithm are the simplified linear combinations of PBPs with physical meanings, and provide quantitative characterization of the three pathological features in different breast tissues respectively. We present results of the three PFPs of tissue samples from a cohort of 32 clinical patients diagnosed as normal, breast fibroma, breast ductal carcinoma in situ, invasive ductal carcinoma, and breast mucinous carcinoma with analysis of 210 regions-of-interest (ROI). The results demonstrate that the three PFPs of each breast disease tissue have specific value ranges, which has a potential to quantitatively distinguish typical pathological features between different breast tissues. This technique has good prospects for automation of the microstructure identification and prediction of breast disease diagnosis, resulting in the reduction of pathologists’ workload.

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