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2,871 results on '"XU Yingying"'

78. PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images

Author

Xu, Yingying, Cai, Ming, Lin, Lanfen, Zhang, Yue, Hu, Hongjie, Peng, Zhiyi, Zhang, Qiaowei, Chen, Qingqing, Mao, Xiongwei, Iwamoto, Yutaro, Han, Xian-Hua, Chen, Yen-Wei, and Tong, Ruofeng

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

In this paper, we propose a phase attention residual network (PA-ResSeg) to model multi-phase features for accurate liver tumor segmentation, in which a phase attention (PA) is newly proposed to additionally exploit the images of arterial (ART) phase to facilitate the segmentation of portal venous (PV) phase. The PA block consists of an intra-phase attention (Intra-PA) module and an inter-phase attention (Inter-PA) module to capture channel-wise self-dependencies and cross-phase interdependencies, respectively. Thus it enables the network to learn more representative multi-phase features by refining the PV features according to the channel dependencies and recalibrating the ART features based on the learned interdependencies between phases. We propose a PA-based multi-scale fusion (MSF) architecture to embed the PA blocks in the network at multiple levels along the encoding path to fuse multi-scale features from multi-phase images. Moreover, a 3D boundary-enhanced loss (BE-loss) is proposed for training to make the network more sensitive to boundaries. To evaluate the performance of our proposed PA-ResSeg, we conducted experiments on a multi-phase CT dataset of focal liver lesions (MPCT-FLLs). Experimental results show the effectiveness of the proposed method by achieving a dice per case (DPC) of 0.77.87, a dice global (DG) of 0.8682, a volumetric overlap error (VOE) of 0.3328 and a relative volume difference (RVD) of 0.0443 on the MPCT-FLLs. Furthermore, to validate the effectiveness and robustness of PA-ResSeg, we conducted extra experiments on another multi-phase liver tumor dataset and obtained a DPC of 0.8290, a DG of 0.9132, a VOE of 0.2637 and a RVD of 0.0163. The proposed method shows its robustness and generalization capability in different datasets and different backbones., Comment: A self-archive version to be published in Medical Physics, awaiting minor revision

Published
2021
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81. High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood

Author

Suotsalo, Kimmo, Xu, Yingying, Corander, Jukka, and Pensar, Johan

Subjects
Statistics - Computation
Abstract

Learning vector autoregressive models from multivariate time series is conventionally approached through least squares or maximum likelihood estimation. These methods typically assume a fully connected model which provides no direct insight to the model structure and may lead to highly noisy estimates of the parameters. Because of these limitations, there has been an increasing interest towards methods that produce sparse estimates through penalized regression. However, such methods are computationally intensive and may become prohibitively time-consuming when the number of variables in the model increases. In this paper we adopt an approximate Bayesian approach to the learning problem by combining fractional marginal likelihood and pseudo-likelihood. We propose a novel method, PLVAR, that is both faster and produces more accurate estimates than the state-of-the-art methods based on penalized regression. We prove the consistency of the PLVAR estimator and demonstrate the attractive performance of the method on both simulated and real-world data.

Published
2020
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100. Benefits of hyperthermic intraperitoneal chemotherapy for patients with serosal invasion in gastric cancer: a meta-analysis of the randomized controlled trials

Author

Sun Jingxu, Song Yongxi, Wang Zhenning, Gao Peng, Chen Xiaowan, Xu Yingying, Liang Jiwang, and Xu Huimian

Subjects
Hyperthermic intraperitoneal chemotherapy, Gastric cancer, Peritoneal recurrence, Meta-analysis, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background In this meta-analysis we aimed to determine the effectiveness and safety of hyperthermic intraperitoneal chemotherapy (HIPC) for patients with advanced gastric cancer who underwent gastrectomy. Methods In accordance with standard meta-analysis procedures, our study included patients who underwent resection for advanced gastric cancer and were randomly allocated to receive either hyperthermic intraperitoneal chemotherapy or control. We searched PubMed (up to November 2011), EMBASE (up to November 2011), Cochrane Database of Systematic Reviews (CDSR), and Cochrane Central Register of Controlled Trials (CCTR) (up to November 2011). Both published and unpublished trials were included in the analysis, and no search restrictions were imposed. There was no language restriction. The results were analyzed using RevMan 5.1 software, which was provided by Cochrane Collaboration. Results There were ten randomized controlled trials included in the analysis. A total of 1062 patients with gastric cancer in these studies were divided into the HIPC group (n = 518) and control group (n = 544). A significant improvement in survival was observed in the HIPC groups compared to the control group in the mitomycin C (MMC) subgroup (RR = 0.75, 95%CI 0.65-0.86; P Conclusions Our meta-analysis demonstrated that HIPC may improve the overall survival rate for patients who receive resection for advance gastric cancer potentially, and help to prevent peritoneal local recurrence among patients with serosal invasion in gastric cancer.

Published
2012
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