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Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network CPCI-S
会议论文 | 2021 , 12908 , 132-142 | International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
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Abstract :

The grade of clear cell renal cell carcinoma (ccRCC) is a critical prognostic factor, making ccRCC nuclei grading a crucial task in RCC pathology analysis. Computer-aided nuclei grading aims to improve pathologists' work efficiency while reducing their misdiagnosis rate by automatically identifying the grades of tumor nuclei within histopathological images. Such a task requires precisely segment and accurately classify the nuclei. However, most of the existing nuclei segmentation and classification methods can not handle the inter-class similarity property of nuclei grading, thus can not be directly applied to the ccRCC grading task. In this paper, we propose a Composite High-Resolution Network for ccRCC nuclei grading. Specifically, we propose a segmentation network called W-Net that can separate the clustered nuclei. Then, we recast the fine-grained nuclei classification into two cross-category classification tasks that are leaned by two newly designed high-resolution feature extractors (HRFEs). The two HRFEs share the same backbone encoder with W-Net by a composite connection so that meaningful features for the segmentation task can be inherited to the classification task. Last, a head-fusion block is applied to generate the predicted label of each nucleus. Furthermore, we introduce a dataset for ccRCC nuclei grading, containing 1000 image patches with 70945 annotated nuclei. We demonstrate that our proposed method achieves state-of-the-art performance compared to existing methods on this large ccRCC grading dataset.

Keyword :

Nuclei grading Histopathology Nuclei segmentation

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GB/T 7714 Gao, Zeyu , Shi, Jiangbo , Zhang, Xianli et al. Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network [C] . 2021 : 132-142 .
MLA Gao, Zeyu et al. "Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network" . (2021) : 132-142 .
APA Gao, Zeyu , Shi, Jiangbo , Zhang, Xianli , Li, Yang , Zhang, Haichuan , Wu, Jialun et al. Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network . (2021) : 132-142 .
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Knowledge enhanced LSTM for coreference resolution on biomedical texts SCIE PubMed
期刊论文 | 2021 , 37 (17) , 2699-2705 | BIOINFORMATICS
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Abstract :

Motivation: Bio-entity Coreference Resolution focuses on identifying the coreferential links in biomedical texts, which is crucial to complete bio-events' attributes and interconnect events into bio-networks. Previously, as one of the most powerful tools, deep neural network-based general domain systems are applied to the biomedical domain with domain-specific information integration. However, such methods may raise much noise due to its insufficiency of combining context and complex domain-specific information. Results: In this article, we explore how to leverage the external knowledge base in a fine-grained way to better resolve coreference by introducing a knowledge-enhanced Long Short Term Memory network (LSTM), which is more flexible to encode the knowledge information inside the LSTM. Moreover, we further propose a knowledge attention module to extract informative knowledge effectively based on contexts. The experimental results on the BioNLP and CRAFT datasets achieve state-of-the-art performance, with a gain of 7.5 F1 on BioNLP and 10.6 F1 on CRAFT. Additional experiments also demonstrate superior performance on the cross-sentence coreferences.

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GB/T 7714 Li, Yufei , Ma, Xiaoyong , Zhou, Xiangyu et al. Knowledge enhanced LSTM for coreference resolution on biomedical texts [J]. | BIOINFORMATICS , 2021 , 37 (17) : 2699-2705 .
MLA Li, Yufei et al. "Knowledge enhanced LSTM for coreference resolution on biomedical texts" . | BIOINFORMATICS 37 . 17 (2021) : 2699-2705 .
APA Li, Yufei , Ma, Xiaoyong , Zhou, Xiangyu , Cheng, Pengzhen , He, Kai , Li, Chen . Knowledge enhanced LSTM for coreference resolution on biomedical texts . | BIOINFORMATICS , 2021 , 37 (17) , 2699-2705 .
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Construction of Genealogical Knowledge Graphs From Obituaries: Multitask Neural Network Extraction System SCIE PubMed
期刊论文 | 2021 , 23 (8) | JOURNAL OF MEDICAL INTERNET RESEARCH
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Abstract :

Background: Genealogical information, such as that found in family trees, is imperative for biomedical research such as disease heritability and risk prediction. Researchers have used policyholder and their dependent information in medical claims data and emergency contacts in electronic health records (EHRs) to infer family relationships at a large scale. We have previously demonstrated that online obituaries can be a novel data source for building more complete and accurate family trees. Objective: Aiming at supplementing EHR data with family relationships for biomedical research, we built an end-to-end information extraction system using a multitask-based artificial neural network model to construct genealogical knowledge graphs (GKGs) from online obituaries. GKGs are enriched family trees with detailed information including age, gender, death and birth dates, and residence. Methods: Built on a predefined family relationship map consisting of 4 types of entities (eg, people's name, residence, birth date, and death date) and 71 types of relationships, we curated a corpus containing 1700 online obituaries from the metropolitan area of Minneapolis and St Paul in Minnesota. We also adopted data augmentation technology to generate additional synthetic data to alleviate the issue of data scarcity for rare family relationships. A multitask-based artificial neural network model was then built to simultaneously detect names, extract relationships between them, and assign attributes (eg, birth dates and death dates, residence, age, and gender) to each individual. In the end, we assemble related GKGs into larger ones by identifying people appearing in multiple obituaries. Results: Our system achieved satisfying precision (94.79%), recall (91.45%), and F-1 measures (93.09%) on 10-fold cross-validation. We also constructed 12,407 GKGs, with the largest one made up of 4 generations and 30 people. Conclusions: In this work, we discussed the meaning of GKGs for biomedical research, presented a new version of a corpus with a predefined family relationship map and augmented training data, and proposed a multitask deep neural system to construct and assemble GKGs. The results show our system can extract and demonstrate the potential of enriching EHR data for more genetic research. We share the source codes and system with the entire scientific community on GitHub without the corpus for privacy protection.

Keyword :

EHR neural network genealogy information extraction genealogical knowledge graph

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GB/T 7714 He, Kai , Yao, Lixia , Zhang, JiaWei et al. Construction of Genealogical Knowledge Graphs From Obituaries: Multitask Neural Network Extraction System [J]. | JOURNAL OF MEDICAL INTERNET RESEARCH , 2021 , 23 (8) .
MLA He, Kai et al. "Construction of Genealogical Knowledge Graphs From Obituaries: Multitask Neural Network Extraction System" . | JOURNAL OF MEDICAL INTERNET RESEARCH 23 . 8 (2021) .
APA He, Kai , Yao, Lixia , Zhang, JiaWei , Li, Yufei , Li, Chen . Construction of Genealogical Knowledge Graphs From Obituaries: Multitask Neural Network Extraction System . | JOURNAL OF MEDICAL INTERNET RESEARCH , 2021 , 23 (8) .
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Scene Attention Mechanism for Remote Sensing Image Caption Generation CPCI-S
会议论文 | 2020 | International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)
WoS CC Cited Count: 1
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Abstract :

Remote sensing images play an important role in various applications. To make it easier for humans to understand remote sensing images, the task of remote sensing image captioning attracts more and more researchers' attention. Inspired from the way human receives visual information, attention mechanism has been widely used in remote sensing image understanding. To catch more scene information and improve the stability of the generated sentences, a new attention mechanism called scene attention is proposed. Except for the current attention via the current hidden state of the long short-term memory network (LSTM), our proposed method simultaneously explores the global visual information from the mean feature of all convolutional features. The effectiveness of the proposed method is evaluated on UCM-captions, Sydney-captions and RSICD datasets. The results of our experiment show that comparing with some other captioning methods, our method is more stable and obtains a better performance.

Keyword :

long short-term memory network scene attention mechanism remote sensing image captioning convolutional neural network

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GB/T 7714 Wu, Shiqi , Zhang, Xiangrong , Wang, Xin et al. Scene Attention Mechanism for Remote Sensing Image Caption Generation [C] . 2020 .
MLA Wu, Shiqi et al. "Scene Attention Mechanism for Remote Sensing Image Caption Generation" . (2020) .
APA Wu, Shiqi , Zhang, Xiangrong , Wang, Xin , Li, Chen , Jiao, Licheng . Scene Attention Mechanism for Remote Sensing Image Caption Generation . (2020) .
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Discriminative Feature Pyramid Network For Object Detection In Remote Sensing Images CPCI-S
会议论文 | 2020 | International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)
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Abstract :

Multi-class geospatial object detection in remote sensing images suffer great challenges, such as large scales variability and complex background. Although feature pyramid network (FPN) can alleviate the problem of scale variation to some extent, it causes the loss of spatial and semantic information which is not conducive to object location. To address the above problem, this paper proposes a discriminative feature pyramid network (DFPN) by introducing a global guidance module (GGM) and a feature aggregation module (FAM). Specifically, the global guidance module delivers the high-level semantic information to lower layers, so as to obtain feature maps with stronger semantic information to eliminate the interference caused by complex background. The feature aggregation module enhances the interfiow of information between different layers and better captures the discrimination information at each layer. We validate the effectiveness of our method on the NWPU VHR-10 and RSOD datasets, the results outperform baseline by 2.06 and 3.88 points respectively.

Keyword :

discriminative feature learning global guidance module feature aggregation module Object detection

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GB/T 7714 Zhu, Xiaoqian , Zhang, Xiangrong , Zhang, Tianyang et al. Discriminative Feature Pyramid Network For Object Detection In Remote Sensing Images [C] . 2020 .
MLA Zhu, Xiaoqian et al. "Discriminative Feature Pyramid Network For Object Detection In Remote Sensing Images" . (2020) .
APA Zhu, Xiaoqian , Zhang, Xiangrong , Zhang, Tianyang , Zhu, Peng , Tang, Xu , Li, Chen . Discriminative Feature Pyramid Network For Object Detection In Remote Sensing Images . (2020) .
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Group target track initiation method aided by echo amplitude information [回波幅度信息辅助的群目标航迹起始方法] Scopus CSCD
期刊论文 | 2020 , 9 (4) , 723-729 | Journal of Radars
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Track initiation is the first important step in group target tracking, and it has a direct effect on the quality of the overall procedure. Traditional radar target tracking methods only utilize information about the target position to detect group numbers, but they do not use information relating to echo amplitude. Tracks are thus easily lost, as the numbers of detected groups and equivalent measurements are inaccurate. This paper proposes a group target track initiation method aided by echo amplitude information to ameliorate these problems. In this respect, target position and echo amplitude information is used to detect the number of target groups, and equivalent measurements are then computed using amplitude weighting and position weighting. Echo amplitude information is employed in the step of detecting group target numbers and computing the equivalent measurements, and group target tracks are subsequently initialized using the modified logic method. The proposed method can be used to correctly detect the number of target groups when the number is previously unknown. Furthermore, the method reduces the rate of track loss and improves the performance of group target tracking. The effectiveness of the proposed method is validated by the simulation results. © 2020 Institute of Electronics Chinese Academy of Sciences. All rights reserved.

Keyword :

Amplitude information; Cluster; Group target; Target tracking; Track initiation

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GB/T 7714 Jin, B. , Li, C. , Zhang, Z. . Group target track initiation method aided by echo amplitude information [回波幅度信息辅助的群目标航迹起始方法] [J]. | Journal of Radars , 2020 , 9 (4) : 723-729 .
MLA Jin, B. et al. "Group target track initiation method aided by echo amplitude information [回波幅度信息辅助的群目标航迹起始方法]" . | Journal of Radars 9 . 4 (2020) : 723-729 .
APA Jin, B. , Li, C. , Zhang, Z. . Group target track initiation method aided by echo amplitude information [回波幅度信息辅助的群目标航迹起始方法] . | Journal of Radars , 2020 , 9 (4) , 723-729 .
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A system for automatically extracting clinical events with temporal information SCIE PubMed
期刊论文 | 2020 , 20 (1) | BMC MEDICAL INFORMATICS AND DECISION MAKING | IF: 2.796
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Abstract :

Background: The popularization of health and medical informatics yields huge amounts of data. Extracting clinical events on a temporal course is the foundation of enabling advanced applications and research. It is a structure of presenting information in chronological order. Manual extraction would be extremely challenging due to the quantity and complexity of the records. Methods: We present an recurrent neural network- based architecture, which is able to automatically extract clinical event expressions along with each event's temporal information. The system is built upon the attention-based and recursive neural networks and introduce a piecewise representation (we divide the input sentences into three pieces to better utilize the information in the sentences), incorporates semantic information by utilizing word representations obtained from BioASQ and Wikipedia. Results: The system is evaluated on the THYME corpus, a set of manually annotated clinical records from Mayo Clinic. In order to further verify the effectiveness of the system, the system is also evaluated on the TimeBank _Dense corpus. The experiments demonstrate that the system outperforms the current state-of-the-art models. The system also supports domain adaptation, i.e., the system may be used in brain cancer data while its model is trained in colon cancer data. Conclusion: Our system extracts temporal expressions, event expressions and link them according to actually occurring sequence, which may structure the key information from complicated unstructured clinical records. Furthermore, we demonstrate that combining the piecewise representation method with attention mechanism can capture more complete features. The system is flexible and can be extended to handle other document types.

Keyword :

Relation extraction Attention mechanism Event extraction Piecewise representation Temporal extraction Clinical text mining

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GB/T 7714 Li, Zhijing , Li, Chen , Long, Yu et al. A system for automatically extracting clinical events with temporal information [J]. | BMC MEDICAL INFORMATICS AND DECISION MAKING , 2020 , 20 (1) .
MLA Li, Zhijing et al. "A system for automatically extracting clinical events with temporal information" . | BMC MEDICAL INFORMATICS AND DECISION MAKING 20 . 1 (2020) .
APA Li, Zhijing , Li, Chen , Long, Yu , Wang, Xuan . A system for automatically extracting clinical events with temporal information . | BMC MEDICAL INFORMATICS AND DECISION MAKING , 2020 , 20 (1) .
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Bio-semantic relation extraction with attention-based external knowledge reinforcement EI SCIE PubMed
期刊论文 | 2020 , 21 (1) | BMC BIOINFORMATICS | IF: 3.169
WoS CC Cited Count: 2
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Background Semantic resources such as knowledge bases contains high-quality-structured knowledge and therefore require significant effort from domain experts. Using the resources to reinforce the information retrieval from the unstructured text may further exploit the potentials of such unstructured text resources and their curated knowledge. Results The paper proposes a novel method that uses a deep neural network model adopting the prior knowledge to improve performance in the automated extraction of biological semantic relations from the scientific literature. The model is based on a recurrent neural network combining the attention mechanism with the semantic resources, i.e., UniProt and BioModels. Our method is evaluated on the BioNLP and BioCreative corpus, a set of manually annotated biological text. The experiments demonstrate that the method outperforms the current state-of-the-art models, and the structured semantic information could improve the result of bio-text-mining. Conclusion The experiment results show that our approach can effectively make use of the external prior knowledge information and improve the performance in the protein-protein interaction extraction task. The method should be able to be generalized for other types of data, although it is validated on biomedical texts.

Keyword :

Knowledge base Bio-text-mining Attention mechanism Biological semantic relation

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GB/T 7714 Li, Zhijing , Lian, Yuchen , Ma, Xiaoyong et al. Bio-semantic relation extraction with attention-based external knowledge reinforcement [J]. | BMC BIOINFORMATICS , 2020 , 21 (1) .
MLA Li, Zhijing et al. "Bio-semantic relation extraction with attention-based external knowledge reinforcement" . | BMC BIOINFORMATICS 21 . 1 (2020) .
APA Li, Zhijing , Lian, Yuchen , Ma, Xiaoyong , Zhang, Xiangrong , Li, Chen . Bio-semantic relation extraction with attention-based external knowledge reinforcement . | BMC BIOINFORMATICS , 2020 , 21 (1) .
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Stacked Residual Recurrent Neural Networks with Cross-Layer Attention for Text Classification EI SCIE
期刊论文 | 2020 , 8 , 70401-70410 | IEEE Access | IF: 3.367
WoS CC Cited Count: 4
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Abstract :

Text classification is a fundamental task in natural language processing and is essential for many tasks like sentiment analysis and question classification etc. As we all know, different NLP tasks require different linguistic features. Tasks such as text classification requires more semantic features than other tasks such as dependency parsing requiring more syntactic features. Most existing methods focus on improving performance by mixing and calibrating features, without distinguishing the types of features and corresponding effects. In this paper, we propose a stacked residual recurrent neural networks with cross-layer attention model to filter more semantic features for text classification, which named SRCLA. Firstly, we build a stacked network structure to filter different types of linguistic features, and then propose a novel cross-layer attention mechanism that exploits higher-level features to supervise the lower-level features to refine the filtering process. Based on this, more semantic features can be selected for text classification. We conduct experiments on eight text classification tasks, including sentiment analysis, question classification and subjectivity classification and compare with a broad range of baselines. Experimental results show that the proposed approaches achieve the state-of-the-art results on 5 out of 8 tasks. © 2020 IEEE.

Keyword :

Recurrent neural networks Multilayer neural networks Semantics Network layers Syntactics Classification (of information) Sentiment analysis

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GB/T 7714 Lan, Yangyang , Hao, Yazhou , Xia, Kui et al. Stacked Residual Recurrent Neural Networks with Cross-Layer Attention for Text Classification [J]. | IEEE Access , 2020 , 8 : 70401-70410 .
MLA Lan, Yangyang et al. "Stacked Residual Recurrent Neural Networks with Cross-Layer Attention for Text Classification" . | IEEE Access 8 (2020) : 70401-70410 .
APA Lan, Yangyang , Hao, Yazhou , Xia, Kui , Qian, Buyue , Li, Chen . Stacked Residual Recurrent Neural Networks with Cross-Layer Attention for Text Classification . | IEEE Access , 2020 , 8 , 70401-70410 .
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Extracting Kinship from Obituary to Enhance Electronic Health Records for Genetic Research CPCI-S
会议论文 | 2019 , 1-10 | 4th Social Media Mining for Health Applications Workshop and Shared Task (SMM4H)
WoS CC Cited Count: 3
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Abstract :

Claims database and electronic health records database do not usually capture kinship or family relationship information, which is imperative for genetic research. We identify online obituaries as a new data source and propose a special named entity recognition and relation extraction solution to extract names and kinships from online obituaries. Built on 1,809 annotated obituaries and a novel tagging scheme, our joint neural model achieved macro-averaged precision, recall and F measure of 72.69%, 78.54% and 74.93%, and micro-averaged precision, recall and F measure of 95.74%, 98.25% and 96.98% using 57 kinships with 10 or more examples in a 10-fold cross-validation experiment. The model performance improved dramatically when trained with 34 kinships with 50 or more examples. Leveraging additional information such as age, death date, birth date and residence mentioned by obituaries, we foresee a promising future of supplementing EHR databases with comprehensive and accurate kinship information for genetic research.

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GB/T 7714 He, Kai , Wu, Jialun , Ma, Xiaoyong et al. Extracting Kinship from Obituary to Enhance Electronic Health Records for Genetic Research [C] . 2019 : 1-10 .
MLA He, Kai et al. "Extracting Kinship from Obituary to Enhance Electronic Health Records for Genetic Research" . (2019) : 1-10 .
APA He, Kai , Wu, Jialun , Ma, Xiaoyong , Zhang, Chong , Huang, Ming , Li, Chen et al. Extracting Kinship from Obituary to Enhance Electronic Health Records for Genetic Research . (2019) : 1-10 .
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