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103 results on '"Gao, Haoqi"'

4. A Hybrid CNN Feature Model for Pulmonary Nodule Differentiation Task

Author

Zhao, Tingting, Wang, Huafeng, Li, Lihong, Qi, Yifang, Gao, Haoqi, Han, FangFang, Liang, Zhengrong, Qi, Yanmin, Cao, Yuan, 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, Tavares, João Manuel R.S., editor, Aylward, Stephen, editor, Li, Shuo, editor, Boctor, Emad, editor, Fichtinger, Gabor, editor, Cleary, Kevin, editor, Freeman, Bradley, editor, Kohli, Luv, editor, Shipley Kane, Deborah, editor, Oetgen, Matt, editor, and Pujol, Sonja, editor

Published
2017
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5. A Novel Texture Extraction Method for the Sedimentary Structures’ Classification of Petroleum Imaging Logging

Author

Gao, Haoqi, Wang, Huafeng, Feng, Zhou, Fu, Mingxia, Ma, Chennan, Pan, Haixia, Xu, Binshen, Li, Ning, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Tan, Tieniu, editor, Li, Xuelong, editor, Chen, Xilin, editor, Zhou, Jie, editor, Yang, Jian, editor, and Cheng, Hong, editor

Published
2016
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19. CGAN-based Synthetic Medical Image Augmentation between Retinal Fundus Images and Vessel Segmented Images

Author

Koichi Ogawara and Gao HaoQi

Subjects
Computer science, business.industry, Patient privacy, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Image segmentation, Fundus (eye), Image (mathematics), Sørensen–Dice coefficient, Clinical diagnosis, Medical imaging, Computer vision, Artificial intelligence, Medical diagnosis, business
Abstract

Computer-aided medical diagnosis is widely used to assist in the interpretation of medical images. Early screening of diseases has a great value for the clinical diagnosis. However, medical images are scarce and fraught with legal concerns about patient privacy. Inspired by the prevailing learning capability of Generative Adversarial Network (GAN), We propose simulated learning: it first learns a mapping from retinal fundus images to vessel segmented images and then trains a learning model to generate synthetic fundus images. In the results, we evaluated our proposed method from both quantitative and qualitative aspects. For generated segmented images, our method achieves a dice coefficient of 0.838 and an AUC of 0.985 on the DRIVE datasets. You can also see more information about our medical image algorithm results at https://github.com/qiqi7788/Bidirectional-Mapping-formedical-image. These results also verify the effectiveness of our algorithm.

Published
2020

21. Kinetic study on copolymerization of ethylene and 1-hexene catalyzed by [Cp.sub.2]Zr[Cl.sub.2]/MAO catalytic system

Author

Wang, Jianfeng, Wang, Li, Gao, Haoqi, Wang, Wenqin, Jiang, Guohua, Dong, Xiaocheng, Wang, Wei, and Feng, Lianfang

Subjects
Ethylene -- Research -- Properties, Engineering and manufacturing industries, Science and technology, Research, Properties
Abstract

The kinetics of ethylene and 1-hexene copolymerization catalyzed by [Cp.sub.2]Zr[Cl.sub.2]/MAO was studied. A new kinetic mathematical model that takes the reactivation effect of MAO into account has been developed. Applying this model, the good agreement between the experimental data and the fitting profiles was achieved. POLYM. ENG. SCI., 47:540-544, 2007. © 2007 Society of Plastics Engineers, INTRODUCTION The utilization of multiple monomers is of profound importance to the polymer manufacturing industry, since it leads to the production of a large number of polymers with a wide [...]

Published
2007

27. A Novel Texture Extraction Method for the Sedimentary Structures’ Classification of Petroleum Imaging Logging

Author

Wang Huafeng, Ma Chennan, Zhou Feng, Gao Haoqi, Fu Mingxia, Binshen Xu, Ning Li, and Pan Haixia

Subjects
Computer science, business.industry, Logging, Texture extraction, Pattern recognition, 02 engineering and technology, Field (computer science), Support vector machine, Identification (information), 020401 chemical engineering, Cognitive level, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, 0204 chemical engineering, Macro, Texture feature, business
Abstract

The technology for reservoir structure identification has become a challenging problem in the field of imaging logging technology. Because of the huge amount of information and a wide variety, it causes experts with low efficiency on the interpretation of reservoir evaluation and the performance depends highly on the individual experience (including cognitive level, visual decision, etc.). We proposed a new method for texture feature extraction based on macro and micro features. About 3320 imaging logging datasets are fed to support vector machine (SVM) to validate the gains of new method. As a result, the new proposed method achieved an Area Under roc Curve (AUC) value of 0.94.

Published
2016

31. Adsorption kinetics and thermodynamics of water-insoluble crosslinked ?-cyclodextrin polymer for phenol in aqueous solution

Author

Huang, Hui, Fan, Yifan, Wang, Junwei, Gao, Haoqi, and Tao, Shiying

Subjects
thermodynamics, adsorption, kinetics, phenol, ?-cyclodextrin
Abstract

A water-insoluble ?-cyclodextrin (?-CD) polymer was synthesized by reacting ?-CD with hexamethyl- ene diisocyanate, and its adsorption kinetics and thermodynamics for phenol from aqueous solution was investi- gated. The kinetics of adsorption followed the pseudo-second-order model and the adsorption isotherms could be well fitted by the Freundlich adsorption equation. The values of thermodynamic parameters demonstrated that the adsorption was a physisorption in a spontaneous and exothermic process.

Published
2013

32. A hybrid CNN feature model for pulmonary nodule malignancy risk differentiation.

Author

Wang, Huafeng, Zhao, Tingting, Li, Lihong Connie, Pan, Haixia, Liu, Wanquan, Gao, Haoqi, Han, Fangfang, Wang, Yuehai, Qi, Yifan, and Liang, Zhengrong

Subjects
LUNG cancer, LUNG diseases, COMPUTED tomography, ARTIFICIAL neural networks
Abstract

The malignancy risk differentiation of pulmonary nodule is one of the most challenge tasks of computer-aided diagnosis (CADx). Most recently reported CADx methods or schemes based on texture and shape estimation have shown relatively satisfactory on differentiating the risk level of malignancy among the nodules detected in lung cancer screening. However, the existing CADx schemes tend to detect and analyze characteristics of pulmonary nodules from a statistical perspective according to local features only. Enlightened by the currently prevailing learning ability of convolutional neural network (CNN), which simulates human neural network for target recognition and our previously research on texture features, we present a hybrid model that takes into consideration of both global and local features for pulmonary nodule differentiation using the largest public database founded by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). By comparing three types of CNN models in which two of them were newly proposed by us, we observed that the multi-channel CNN model yielded the best discrimination in capacity of differentiating malignancy risk of the nodules based on the projection of distributions of extracted features. Moreover, CADx scheme using the new multi-channel CNN model outperformed our previously developed CADx scheme using the 3D texture feature analysis method, which increased the computed area under a receiver operating characteristic curve (AUC) from 0.9441 to 0.9702. [ABSTRACT FROM AUTHOR]

Published
2018
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39. Copolymerization of norbornene, styrene, and naleic anhydride catalyzed by Fe(acac)−Al( i-Bu).

Author

Xu, Weihong, Fang, Jianghua, Cong, Yang, Yang, Jianping, Hu, Minjie, Cai, Guangming, and Gao, Haoqi

Subjects
COPOLYMERIZATION, STYRENE, GEL permeation chromatography, CATALYSTS, FOURIER transform infrared spectroscopy
Abstract

Copolymerization of norbornene (NB), styrene (ST), and maleic anhydride (MA) catalyzed by Fe(acac)-Al( i-Bu) has been investigated at different conditions and Fe/Al ratios in the catalyst. The copolymer composition has been investigated by using C NMR, FTIR, and gel permeation chromatography. Results demonstrate that the Fe(acac)-Al( i-Bu) catalysts are effective for the copolymerization of NB, ST, and MA at mild conditions. [ABSTRACT FROM AUTHOR]

Published
2015
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