Ms. Anli Hou Profile

Anli Hou

at Univ of Chinese Academy of Sciences Shenzhen Hospital

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Proceedings Article | 5 March 2021 Presentation + Paper

Detecting cervical intraepithelial neoplasia using polarimetry parameters and multichannel convolutional neural network

Yang Dong, Shan Du, Anli Hou, Hui Ma

Proceedings Volume 11646, 1164611 (2021) https://doi.org/10.1117/12.2577581

KEYWORDS: Polarimetry, Convolutional neural networks, Statistical modeling, Cervical cancer, Tissues, Statistical analysis, Performance modeling, Microscopes, Biopsy

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Early diagnosis and fast screening of cervical cancer is the key to prognosis of treatment and patient survival. Polarimetry technique with high sensitivity to microstructures and low requirement for resolution is promising at facilitating the fast screening and quantitative diagnosis. In this study, we apply the Mueller matrix microscope and multichannel convolutional neural network for the detection of human cervical intraepithelial neoplasia (CIN) samples from normal samples. The Mueller matrix polar decomposition and transformation parameters, rotation invariant parameters, and Mueller matrix symmetry-related parameters of the cervical tissues in epithelial region and at different stages are calculated and analyzed. For detection of early cervical lesions, the selection method of polarimetry parameters based on statistical features and multichannel convolutional neural network (CNN) for classification are proposed. To illustrate, we select the input parameters of CNN models from all commonly used polarimetry parameters according to the amount of information which are evaluated by the mean value, standard deviation, and information entropy of all pixels in 2D parameters images of the training samples. In multichannel CNN classification, each selected parameter is treated as an input of a channel. The proper multichannel CNN models learn deep features from the selected polarimetry parameters of training samples and show good performance for detecting CIN samples under a low-resolution system.

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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