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1,361 results on '"Wang, Tianfu"'

151. Self-weighted Multi-task Learning for Subjective Cognitive Decline Diagnosis

Author

Cheng, Nina, primary, Frangi, Alejandro, additional, Zhang, Zhi-Guo, additional, Deng, Denao, additional, Zhao, Lihua, additional, Wang, Tianfu, additional, Wei, Yichen, additional, Yu, Bihan, additional, Mai, Wei, additional, Duan, Gaoxiong, additional, Nong, Xiucheng, additional, Li, Chong, additional, Su, Jiahui, additional, and Lei, Baiying, additional

Published
2020
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158. Skin Lesion Segmentation via Deep RefineNet

Author

He, Xinzi, Yu, Zhen, Wang, Tianfu, Lei, Baiying, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Cardoso, M. Jorge, editor, Arbel, Tal, editor, Carneiro, Gustavo, editor, Syeda-Mahmood, Tanveer, editor, Tavares, João Manuel R.S., editor, Moradi, Mehdi, editor, Bradley, Andrew, editor, Greenspan, Hayit, editor, Papa, João Paulo, editor, Madabhushi, Anant, editor, Nascimento, Jacinto C., editor, Cardoso, Jaime S., editor, Belagiannis, Vasileios, editor, and Lu, Zhi, editor

Published
2017
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159. Aggregating Deep Convolutional Features for Melanoma Recognition in Dermoscopy Images

Author

Yu, Zhen, Jiang, Xudong, Wang, Tianfu, Lei, Baiying, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Wang, Qian, editor, Shi, Yinghuan, editor, Suk, Heung-Il, editor, and Suzuki, Kenji, editor

Published
2017
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168. Bridging Computational Features Toward Multiple Semantic Features with Multi-task Regression: A Study of CT Pulmonary Nodules

Author

Chen, Sihong, Ni, Dong, Qin, Jing, Lei, Baiying, Wang, Tianfu, Cheng, Jie-Zhi, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ourselin, Sebastien, editor, Joskowicz, Leo, editor, Sabuncu, Mert R., editor, Unal, Gozde, editor, and Wells, William, editor

Published
2016
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169. Automatic Cystocele Severity Grading in Ultrasound by Spatio-Temporal Regression

Author

Ni, Dong, Ji, Xing, Gao, Yaozong, Cheng, Jie-Zhi, Wang, Huifang, Qin, Jing, Lei, Baiying, Wang, Tianfu, Wu, Guorong, Shen, Dinggang, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ourselin, Sebastien, editor, Joskowicz, Leo, editor, Sabuncu, Mert R., editor, Unal, Gozde, editor, and Wells, William, editor

Published
2016
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170. Adaptive Context Aggregation Network With Prediction-Aware Decoding for Multimodal Brain Tumor Segmentation

Author

Yue, Guanghui, Wu, Shangjie, Du, Jie, Zhou, Tianwei, Jiang, Bin, and Wang, Tianfu

Abstract

Precise segmentation of brain tumor from magnetic resonance images (MRIs) is of vital importance for lesion measurement, surgical planning decision, and clinical treatment evaluation. In the context of rapid development of deep neural networks (DNNs), an increasing number of DNN-based brain tumor segmentation methods have been recently reported. However, most methods usually ignore the interaction among multimodal data and multiscale context features, resulting in poor segmentation accuracy on challenging cases, e.g., tumors with complex interactions between subregions. This article proposes a novel adaptive context aggregation network (ACANet) for multimodal brain tumor segmentation. ACANet follows a parallel encoder-decoder framework. Specifically, to bridge the gaps between different modalities, we propose an adaptive context aggregation (ACA) module that adaptively fuses multimodal features from the encoders of two input branches to explore their complementary information and aggregates multiscale context information extracted by a set of dilated convolutions. To cope with the complex boundary interactions, we propose a prediction-aware decoding strategy (PDS) at the decoder to help the network focus more on error-prone regions through the guidance of two temporal predictions when incorporating the features from the encoder. Extensive experiments on two brain tumor segmentation datasets demonstrate the superiority of ACANet over eight mainstream methods. Additionally, a series of ablation studies supports the effectiveness of ACANet’s underlying concepts.

Published
2024
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171. Deep Pyramid Network for Low-Light Endoscopic Image Enhancement

Author

Yue, Guanghui, Gao, Jie, Cong, Runmin, Zhou, Tianwei, Li, Leida, and Wang, Tianfu

Abstract

Endoscopic images captured under low-light enclosed intestinal environment usually have poor visibility (manifested as uneven illumination and noise), affecting the work efficiency of physicians and the accuracy of lesion detection. To improve the image quality, the literature has reported many low-light image enhancement (LIE) methods. However, most methods do not perform well in handling the low-light endoscopic image enhancement (LEIE) task, usually bringing additional artifacts or amplifying noise. In this paper, we propose a novel deep pyramid enhancement network (DPENet) to enhance endoscopic images from both global and local perspectives. Specifically, considering the uneven illumination of endoscopic images, DPENet utilizes an image pyramid framework with three parallel branches to explore and integrate both global and local features at different scales. To suppress noise, DPENet sets multiple scale-space feature extraction blocks (SFEBs) in each branch. SFEB consists of a contextual feature extraction module (CFEM) and a spatial residual attention module (SRAM). CFEM mines contextual information to help the network understand semantic information while suppress the isolated noise. SRAM leverages the spatial attention mechanism to help the network adaptively focus on dim regions. Experimental results on a public dataset and our collected dataset show that DPENet is competent for the LEIE task with promising results, and outperforms 9 state-of-the-art LIE methods in both qualitative and quantitative aspects.

Published
2024
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190. Joint Learning of Multiple Longitudinal Prediction Models by Exploring Internal Relations

Author

Lei, Baiying, Chen, Siping, Ni, Dong, Wang, Tianfu, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Zhou, Luping, editor, Wang, Li, editor, Wang, Qian, editor, and Shi, Yinghuan, editor

Published
2015
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191. Bringing Categories Back In: Institutional Factors of Income Inequality in Urban China

Author

Wang, Feng and Wang, Tianfu

Subjects
democratic
Abstract

In this paper, we examine the roles of institutional sources in the spectacular rise of income inequality in urban China during the reform era. Categories created under the state socialism not only formed an institutional basis that defined inequality under socialism, their new life in an emerging market economy have also made them the institutional sources of rising economic inequality. In addition to rewarding individuals differently according to their attributes, income distribution in urban China shows a clear pattern of alignment along boundaries such as locality, industrial sector, ownership type, and work organizations. Relying on mechanisms such as exclusion and opportunity hoarding, and by forming local corporatism and work organization ownership, members in different categories retain a degree of equality while inequality between categories rose sharply. These structural features have resulted in a juxtaposition of a sharply increased inequality between groups and a relative degree of equality within groups.

Published
2003

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