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| 1 | Three‐dimensional neural differentiation of embryonic stem cells with ACM induction in microfibrous matrices in bioreactors显示文摘 | Ning Liu Anli Ouyang Yan Li Shang‐Tian Yang | 2013 | Biotechnol Progress2013，，4： | 2 |
| 2 | 显示文摘 | Yang Anli Cui Zuolin | 2006 | Materials Letters2006，，60： | 1 |
| 3 | Expression profile of long non -coding RNAs is altered in endometrial cancer 显示文摘 | YANG Lin ZHANG Jie JIANG Anli | 2015 | International Journal of Clinical and Experimental Medicine2015，8，4： | 1 |
| 4 | Controlling the orientation of ZnO nanorod arrays using TiO2 thin film templates dip-coated by sol–gel显示文摘 | Anli Yang Zuolin Cui | 2007 | Journal of Nanoparticle Research2007，，2： | 1 |
| 5 | Polarimetry feature parameter deriving from Mueller matrix imaging and auto-diagnostic signicance to distinguish HSIL and CSCC显示文摘High-grade squamous intraepithelial lesion(HSIL)is regarded as a serious precancerous state of cervix,and it is easy to progress into cervical invasive carcinoma which highlights the importance of earlier diagnosis and treatment of cervical lesions.Pathologists examine the biopsied cervical epithelial tissue through a microscope.The pathological examination will take a long time and sometimes results in high inter-and intra-observer variability in outcomes.Polarization imaging techniques have broad application prospects for biomedical diagnosis such as breast,liver,colon,thyroid and so on.In our team,we have derived polarimetry feature parameters(PFPs)to characterize microstructural features in histological sections of breast tissues,and the accuracy for PFPs ranges from 0.82 to 0.91.Therefore,the aim of this paper is to distinguish automatically microstructural features between HSIL and cervical squamous cell carcinoma(CSCC)by means of polarization imaging techniques,and try to provide quantitative reference index for patho-logical diagnosis which can alleviate the workload of pathologists.Polarization images of the H&E stained histological slices were obtained by Mueller matrix microscope.The typical path-ological structure area was labeled by two experienced pathologists.Calculate the polarimetry basis parameter(PBP)statistics for this region.The PBP statistics(stat PBPs)are screened by mutual information(MI)method.The training method is based on a linear discriminant analysis(LDA)classier whichnds the most simplied linear combination from these stat PBPs and the accuracy remains constant to characterize the specic microstructural feature quantitatively in cervical squamous epithelium.We present results from 37 clinical patients with analysis regions of cervical squamous epithelium.The accuracy of PFP for recognizing HSIL and CSCC was 83.8%and 87.5%,respectively.This work demonstrates the ability of PFP to quantitatively charac-terize the cervical squamous epithelial lesions in the H&E pathological sections.Signicance:Polarization detection technology provides an effcient method for digital pathological diagnosis and points out a new way for automatic screening of pathological sections. | Anli Hou Xingjian Wang Yujuan Fan Wenbin Miao Yang Dong Xuewu Tian Jibin Zou Hui Ma | 2022 | Journal of Innovative Optical Health Sciences2022，15，1： | 0 |
| 6 | Kernel-based adversarial attacks and defenses on support vector classification显示文摘While malicious samples are widely found in many application fields of machine learning,suitable countermeasures have been investigated in the field of adversarial machine learning.Due to the importance and popularity of Support Vector Machines(SVMs),we first describe the evasion attack against SVM classification and then propose a defense strategy in this paper.The evasion attack utilizes the classification surface of SVM to iteratively find the minimal perturbations that mislead the nonlinear classifier.Specially,we propose what is called a vulnerability function to measure the vulnerability of the SVM classifiers.Utilizing this vulnerability function,we put forward an effective defense strategy based on the kernel optimization of SVMs with Gaussian kernel against the evasion attack.Our defense method is verified to be very effective on the benchmark datasets,and the SVM classifier becomes more robust after using our kernel optimization scheme. | Wanman Li Xiaozhang Liu Anli Yan Jie Yang | 2022 | Digital Communications and Networks2022，8，4： | 0 |
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