中国科学院机构知识库网格系统: 检索

A Secure Medical Information Storage and Sharing Method Based on Multiblockchain Architecture 期刊论文 OAI收割
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, 2024, 页码: 15
作者: Hu, Jian; Zhu, Peng; Li, Juanjuan; Qi, Yong; Xia, Youbing

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收藏 | 浏览/下载：21/0 | 提交时间：2024/07/03

Medical diagnostic imaging Secure storage Consensus protocol Hospitals Medical services Memory Insurance Blockchain information storage and sharing medical information multiblockchain architecture

A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue Information Extraction 期刊论文 OAI收割
Machine Intelligence Research, 2024, 卷号: 21, 期号: 1, 页码: 153-168
作者: Zefa Hu; Ziyi Ni; Jing Shi; Shuang Xu; Bo Xu

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收藏 | 浏览/下载：10/0 | 提交时间：2024/04/23

Medical dialogue understanding, information extraction, text generation, knowledge-enhanced prompt, low-resource setting, data augmentation

Medical visual question answering with symmetric interaction attention and cross-modal gating 期刊论文 OAI收割
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2023, 卷号: 85, 页码: 10
作者: Chen, Zhi; Zou, Beiji; Dai, Yulan; Zhu, Chengzhang; Kong, Guilan

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收藏 | 浏览/下载：28/0 | 提交时间：2023/11/17

Medical visual question answering Self-attention Information interaction Cross-modal gating

Intelligent city intelligent medical sharing technology based on internet of things technology 期刊论文 OAI收割
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2020, 卷号: 111, 页码: 226-233
作者: Zhao, Xin; Xiao, Wei; Wu, Lu; Zhao, Zhigang; Huo, Jidong

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收藏 | 浏览/下载：49/0 | 提交时间：2020/12/10

Internet of things Intelligent city Intelligent medical sharing Data encryption Information security

Knowledge Representation in Patient Safety Reporting: An Ontological Approach 期刊论文 OAI收割
journal of data and information science, 2016, 卷号: 1, 期号: 2, 页码: 75-91
作者: Liang Chen; Yang Gong

收藏 | 浏览/下载：38/0 | 提交时间：2016/06/16

Patient safety Medical error Knowledge representation Health information technology Ontology

The Design of the Integration Multifunction Terminal for Health Management Based on Android System 会议论文 OAI收割
2015 Joint International Mechanical, Electronic and Information Technology Conference (JIMET 2015), Chongqing, China, December 18-20, 2015
作者: Ye D(叶鼎); Shi G(石刚); Liu B(刘博)

收藏 | 浏览/下载：25/0 | 提交时间：2015/12/19

Health Information Collection Terminal Android System Intelligent Medical and Chronic Management

An improved MAC protocol for wireless sensor networks in medical application 会议论文 OAI收割
2012 3rd International Conference on Information Technology for Manufacturing Systems, ITMS 2012, Qingdao, China, September 8-9, 2012
作者: Zhao W(赵伟); Shi G(石刚)

收藏 | 浏览/下载：32/0 | 提交时间：2013/04/21

Energy conservation Information technology Manufacture Medical applications Medium access control Standards

A parallel algorithm for medical images registration based on B-splines (EI CONFERENCE) 会议论文 OAI收割
4th International Congress on Image and Signal Processing, CISP 2011, October 15, 2011 - October 17, 2011, Shanghai, China
作者: Zhang T.

收藏 | 浏览/下载：31/0 | 提交时间：2013/03/25

Cubic B-splines is widely applied in non-rigid registration because of its approximation performance and fast computational characteristics. However a small scale non-rigid deformation is needed to characterize by a large number of control points. Moreover an iterative optimization strategy of the non-rigid registration algorithm and the normalized mutual information (NMI) cost a great quantity calculation. So the process of the non-rigid registration is slowed by calculations of NMI in a iterative optimization strategy. In this paper a parallel optimization algorithm based on cubic B-splines functions is proposed to parallelize the optimization algorithm of the nonrigid registration and the calculations of normalize mutual information. In practice a fast algorithm of cubic B-splines is used and the control points are only distributed on the targets. Experiments show that the use of the fast algorithm and the parallel optimization strategy improves the non-rigid registration process of medical images. 2011 IEEE.

Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China
Wu Z.-G.; Wang M.-J.; Han G.-L.

收藏 | 浏览/下载：81/0 | 提交时间：2013/03/25

Being an efficient method of information fusion image fusion has been used in many fields such as machine vision medical diagnosis military applications and remote sensing.In this paper Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing including segmentation target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First the two original images are decomposed by wavelet transform. Then based on the PCNN a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore by this algorithm the threshold adjusting constant is estimated by appointed iteration number. Furthermore In order to sufficient reflect order of the firing time the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules the experiments upon Multi-focus image are done. Moreover comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.

医学地理和环境健康研究的主要领域与进展 中文期刊论文 OAI收割
2010
作者: 李永华; 王五一; 李海蓉; 杨林生

收藏 | 浏览/下载：27/0 | 提交时间：2012/05/19

medical geography environmental health global environment change health risk assessment geographical information system