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information extraction and CT reconstruction of liver images based on diffraction enhanced imaging

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Progress in Natural Science:Materials International 2009年第8期19卷 955-962页

作者： Chunhong Hua,b, Tao Zhao a, Lu Zhanga, Hui Lia, Xinyan Zhao c, Shuqian Luoa, a College of Biomedical Engineering, Capital Medical University, Beijing 100069, China b National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China c Liver Research Center, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China [a]College of Biomedical Engineering Capital Medical University Beijing 100069 China [b]National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100080 China [c]Liver Research Center Beijing Friendship Hospital Capital Medical University Beijing 100050 China

X-ray phase-contrast imaging (PCI) is a new emerging imaging technique that generates a high spatial resolution and high contrast of biological soft tissues compared to conventional radiography. Herein a biomedical application of diffraction enhanced imaging (DEI) is presented. As one of the PCI methods, DEI derives contrast from many different kinds of sample information, such as the sample’s X-ray absorption, refraction gradient and ultra-small-angle X-ray scattering (USAXS) properties, and the sample information is expressed by three parametric images. Combinedwith computed tomography (CT), DEI-CT can produce 3D volumetric images of the sample and can be used for investigating micro-structures of biomedical samples. Our DEI experiments for liver samples were implemented at the topography station of Beijing Synchrotron Radiation Facility (BSRF). The results show that by using our provided information extraction method and DEI-CT reconstruction approach, the obtained parametric images clearly display the inner structures of liver tissues and the morphology of blood vessels. Furthermore, the reconstructed 3D view of the liver blood vessels exhibits the micro blood vessels whose minimum diameter is on the order of about tens of microns, much better than its conventional CT reconstruction at a millimeter resolution. In conclusion, both the information extraction method and DEI-CT have the potential for use in biomedical micro-structures analysis.

关键词： Diffraction enhanced imaging Computed tomography information extraction X-ray Liver

Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning

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Journal of Mountain Science 2017年第4期14卷 731-741页

作者： LU Heng FU Xiao LIU Chao LI Long-guo HE Yu-xin LI Nai-wen State Key Laboratory of Hydraulics and Mountain River Engineering Sichuan UniversityChengdu 610065China College of Hydraulic and Hydroelectric Engineering Sichuan UniversityChengdu 610065China Key Laboratory of Geo-speeial information Technology Ministry of Land and ResourcesChengdu University of TechnologyChengdu 610059China Faculty of Geosciences and Environmental Engineering Southwest Jiaotong UniversityChengdu 611756China

The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle （UAV） low-height remote sensing technique, which is flexible, efficient with low cost and with high resolution, is widely applied to investing various resources. Based on this, a novel extraction method for cultivated land information based on Deep Convolutional Neural Network and Transfer Learning （DTCLE） was proposed. First, linear features （roads and ridges etc.） were excluded based on Deep Convolutional Neural Network （DCNN）. Next, feature extraction method learned from DCNN was used to cultivated land information extraction by introducing transfer learning mechanism. Last, cultivated land information extraction results were completed by the DTCLE and eCognifion for cultivated land information extraction （ECLE）. The location of the Pengzhou County and Guanghan County, Sichuan Province were selected for the experimental purpose. The experimental results showed that the overall precision for the experimental image 1, 2 and 3 （of extracting cultivated land） with the DTCLE method was 91.7%, 88.1% and 88.2% respectively, and the overall precision of ECLE is 9o.7%, 90.5% and 87.0%, respectively. Accuracy of DTCLE was equivalent to that of ECLE, and also outperformed ECLE in terms of integrity and continuity.

关键词： Unmanned aerial vehicle Cultivated land Deep convolutional neural network Transfer learning information extraction

ESDC:An open Earth science data corpus to support geoscientific literature information extraction

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Science China Earth Sciences 2024年第12期67卷 3840-3854页

作者： Hao LI Peng YUE Deodato TAPETE Francesca CIGNA Qiuju WU Longgang XIANG Binbin LU School of Remote Sensing and information Engineering Wuhan UniversityWuhan 430079China Italian Space Agency Rome 00133Italy National Research Council Institute of Atmospheric Sciences and ClimateRome 00133Italy School of Geography and Environmental Sciences Zhejiang Normal UniversityJinhua 321004China State Key Laboratory of information Engineering in Surveying Mapping and Remote Sensing Wuhan UniversityWuhan 430079China

Over the past ten years,large amounts of original research data related to Earth system science have been made available at a rapidly increasing *** growing data stock helps researchers understand the human-Earth system across different fields.A substantial amount of this data is published by geoscientists as open-access in authoritative *** the information stored in this literature is properly extracted,there is significant potential to build a domain knowledge ***,this potential remains largely unfulfilled in geoscience,with one of the biggest obstacles being the lack of publicly available related corpora and *** fill this gap,the Earth Science Data Corpus(ESDC),an academic text corpus of 600 abstracts,was built from the international journal Earth System Science Data(ESSD).To the best of our knowledge,ESDC is the first corpus with the needed detail to provide a professional training dataset for knowledge extraction and construction of domain-specific knowledge graphs from massive amounts of *** production process of ESDC incorporates both the contextual features of spatiotemporal entities and the linguistic characteristics of academic ***,annotation guidelines and procedures tailored for Earth science data are formulated to ensure *** with zero-and few-shot prompting,BARTNER generative,and W2NER discriminative models were trained on ESDC to evaluate the performance of the name entity recognition task and showed increasing performance metrics,with the highest achieved by *** metrics for various entity types output by each model were also *** utilized the trained BARTNER model to perform model inference on a larger unlabeled literature corpus,aiming to automatically extract a broader and richer set of entity ***,the extracted entity information was mapped and associated with the Earth science data knowledge *** this knowledge graph,thi

关键词： Earth science data Corpus information extraction Knowledge graph Scientometric research

A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue information extraction

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Machine Intelligence Research 2024年第1期21卷 153-168页

作者： Zefa Hu Ziyi Ni Jing Shi Shuang Xu Bo Xu School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Institute of Automation Chinese Academy of SciencesBeijing 100190China

This paper focuses on term-status pair extraction from medical dialogues(MD-TSPE),which is essential in diagnosis dia-logue systems and the automatic scribe of electronic medical records(EMRs).In the past few years,works on MD-TSPE have attracted increasing research attention,especially after the remarkable progress made by generative ***,these generative methods output a whole sequence consisting of term-status pairs in one stage and ignore integrating prior knowledge,which demands a deeper un-derstanding to model the relationship between terms and infer the status of each *** paper presents a knowledge-enhanced two-stage generative framework(KTGF)to address the above *** task-specific prompts,we employ a single model to com-plete the MD-TSPE through two phases in a unified generative form:We generate all terms the first and then generate the status of each generated *** this way,the relationship between terms can be learned more effectively from the sequence containing only terms in the first phase,and our designed knowledge-enhanced prompt in the second phase can leverage the category and status candidates of the generated term for status ***,our proposed special status"not mentioned"makes more terms available and en-riches the training data in the second phase,which is critical in the low-resource *** experiments on the Chunyu and CMDD datasets show that the proposed method achieves superior results compared to the state-of-the-art models in the full training and low-re-sourcesettings.

关键词： Medical dialogue understanding information extraction text generation knowledge-enhanced prompt low-resource setting dataaugmentation

Semantic information extraction from Multi-Corpora Using Deep Learning

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Computers, Materials & Continua 2022年第3期70卷 5021-5038页

作者： Sunil Kumar Hanumat G.Sastry Venkatadri Marriboyina Hammam Alshazly Sahar Ahmed Idris Madhushi Verma Manjit Kaur School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Amity School of Engineering and Technology Amity UniversityGwalior474003India Faculty of Computers and information South Valley UniversityQena83523Egypt College of Industrial Engineering King Khalid UniversityAbhaSaudi Arabia Department of Computer Science Engineering Bennett UniversityGreater Noida201310India

information extraction plays a vital role in natural language processing,to extract named entities and events from unstructured *** to the exponential data growth in the agricultural sector,extracting significant information has become a challenging *** existing deep learningbased techniques have been applied in smart agriculture for crop cultivation,crop disease detection,weed removal,and yield production,still it is difficult to find the semantics between extracted information due to unswerving effects of weather,soil,pest,and fertilizer *** paper consists of two *** initial phase,which proposes a data preprocessing technique for removal of ambiguity in input corpora,and the second phase proposes a novel deep learning-based long short-term memory with rectification in Adam optimizer andmultilayer perceptron to find agricultural-based named entity recognition,events,and relations between *** proposed algorithm has been trained and tested on four input corpora i.e.,agriculture,weather,soil,and pest&*** experimental results have been compared with existing techniques and itwas observed that the proposed algorithm outperformsWeighted-SOM,LSTM+RAO,PLR-DBN,KNN,and Na飗e Bayes on standard parameters like accuracy,sensitivity,and specificity.

关键词： Agriculture deep learning information extraction weather soil

Word Embedding Bootstrapped Deep Active Learning Method to information extraction on Chinese Electronic Medical Record

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Journal of Shanghai Jiaotong university(Science) 2021年第4期26卷 494-502页

作者： MA Qunsheng CEN Xingxing YUAN Junyi HOU Xumin Shanghai Chest Hospital Shanghai Jiao Tong UniversityShanghai200030China

Electronic medical record (EMR) containing rich biomedical information has a great potential in disease diagnosis and biomedical research. However, the EMR information is usually in the form of unstructured text, which increases the use cost and hinders its applications. In this work, an effective named entity recognition (NER) method is presented for information extraction on Chinese EMR, which is achieved by word embedding bootstrapped deep active learning to promote the acquisition of medical information from Chinese EMR and to release its value. In this work, deep active learning of bi-directional long short-term memory followed by conditional random field (Bi-LSTM+CRF) is used to capture the characteristics of different information from labeled corpus, and the word embedding models of contiguous bag of words and skip-gram are combined in the above model to respectively capture the text feature of Chinese EMR from unlabeled corpus. To evaluate the performance of above method, the tasks of NER on Chinese EMR with “medical history” content were used. Experimental results show that the word embedding bootstrapped deep active learning method using unlabeled medical corpus can achieve a better performance compared with other models.

关键词： deep active learning named entity recognition(NER) information extraction word embedding Chinese electronic medical record(EMR)

A Classification Method for Web information extraction

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Wuhan University Journal of Natural Sciences 2004年第5期9卷 823-827页

作者： LIXiang-yang ZHANGYa-fei LUJian-jiang XUBao-wen InstituteotCommunicationsEngineering People＇sLiberationArmyUniversityof〈cienceandTechndogyNan-jing210007JiangsuChina DepartmentofComputerScienceandEngineering.SoutheastUniversity.Nanjing210096.Jiangsu China

Web information extraction is viewed as a classification process and a competing classification method is presented to extract Web information directly through classification. Web fragments are represented with three general features and the similarities between fragments are then defined on the bases of these features. Through competitions of fragments for different slots in information templates, the method classifies fragments into slot classes and filters out noise information. Far less annotated samples are needed as compared with rule-based methods and therefore it has a strong portability. Experiments show that the method has good performance and is superior to DOM-based method in information extraction. Key words information extraction - competing classification - feature extraction - wrapper induction CLC number TP 311 Foundation item: Supported by the National Natural Science Foundation of China (60303024)Biography: LI Xiang-yang (1974-), male, Ph. D. Candidate, research direction: information extraction, natural language processing.

关键词： information extraction competing classification feature extraction wrapper induction

Web-Based information extraction Technology

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Journal of Donghua University(English Edition) 2007年第2期24卷 288-292页

作者：孙铁利教巍巍刘淑华 School of Computer Science Northeast Normal University School of Computer Science and Engineering Liaoning University of Technology

information extraction techniques on the Web are the current research hotspot. Now many information extraction techniques based on different principles have appeared and have different capabilities. We classify the existing information extraction techniques by the principle of information extraction and analyze the methods and principles of semantic information adding, schema defining, rule expression, semantic items locating and object locating in the approaches. Based on the above survey and analysis, several open problems are discussed.

关键词： HTML XML rule semantic information extraction Hidden Markov model

Enhanced Pattern Representation in information extraction

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Journal of Beijing Institute of Technology 2004年第2期13卷 143-147页

作者：廖乐健曹元大张映波 School of information Science and Technology Beijing Institute of TechnologyBeijing100081China

Traditional pattern representation in information extraction lack in the ability of representing domain-specific concepts and are therefore devoid of flexibility. To overcome these restrictions, an enhanced pattern representation is designed which includes ontological concepts, neighboring-tree structures and soft constraints. An information-(extraction) inference engine based on hypothesis-generation and conflict-resolution is implemented. The proposed technique is successfully applied to an information extraction system for Chinese-language query front-end of a job-recruitment search engine.

关键词： information extraction ontology pattern rules