具有乳头样核特征的非浸润性甲状腺滤泡性肿瘤(non-invasive follicular thyroid neoplasm with papillary-like nuclear features,NIFTP)已被纳入WHO(2017)内分泌肿瘤分类。NIFTP的提出对甲状腺细胞学TBS报告系统(the Bethesda system ...具有乳头样核特征的非浸润性甲状腺滤泡性肿瘤(non-invasive follicular thyroid neoplasm with papillary-like nuclear features,NIFTP)已被纳入WHO(2017)内分泌肿瘤分类。NIFTP的提出对甲状腺细胞学TBS报告系统(the Bethesda system for reporting thyroid cytopathology,TBSRTC)有重要意义,TBSRTC(2017版)重点针对NIFTP在中间型及恶性分类的诊断标准、恶性风险值及处理建议作了相应修订。该文重点就NIFTP对甲状腺细胞学的影响及TBSRTC(2017版)对NIFTP的相应处理作一综述。展开更多
In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis ...In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.展开更多
文摘具有乳头样核特征的非浸润性甲状腺滤泡性肿瘤(non-invasive follicular thyroid neoplasm with papillary-like nuclear features,NIFTP)已被纳入WHO(2017)内分泌肿瘤分类。NIFTP的提出对甲状腺细胞学TBS报告系统(the Bethesda system for reporting thyroid cytopathology,TBSRTC)有重要意义,TBSRTC(2017版)重点针对NIFTP在中间型及恶性分类的诊断标准、恶性风险值及处理建议作了相应修订。该文重点就NIFTP对甲状腺细胞学的影响及TBSRTC(2017版)对NIFTP的相应处理作一综述。
基金Project(61101185) supported by the National Natural Science Foundation of ChinaProject(2011AA1221) supported by the National High Technology Research and Development Program of China
文摘In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.
