检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Shi, Yinghuan Yang, Wanqi Thung, Kim-Han Wang, Hao Gao, Yang Pan, Yang Zhang, Li Shen, Dinggang State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University China School of Computer Science Nanjing Normal University China Department of Radiology BRIC UNC Chapel Hill United States Inception Institute of Artificial Intelligence Korea Republic of Nanjing Medical University Nanjing Brain Hospital China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai China Department of Brain and Cognitive Engineering Korea University United Arab Emirates

In this paper, we study a novel problem: "automatic prescription recommendation for PD patients." To realize this goal, we first build a dataset by collecting 1) symptoms of PD patients, and 2) their prescription drug provided by neurologists. Then, we build a novel computer-aided prescription model by learning the relation between observed symptoms and prescription drug. Finally, for the new coming patients, we could recommend (predict) suitable prescription drug on their observed symptoms by our prescription model. From the methodology part, our proposed model, namely Prescription viA Learning lAtent Symptoms (PALAS), could recommend prescription using the multi-modality representation of the data. In PALAS, a latent symptom space is learned to better model the relationship between symptoms and prescription drug, as there is a large semantic gap between them. Moreover, we present an efficient alternating optimization method for PALAS. We evaluated our method using the data collected from 136 PD patients at Nanjing Brain Hospital, which can be regarded as a large dataset in PD research community. The experimental results demonstrate the effectiveness and clinical potential of our method in this recommendation task, if compared with other competing methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Large dataset

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Tripletunet: Multi-Task u-net with online voxel-wise learning for precise ct prostate segmentation

arXiv

引用

arXiv 2020年

作者： He, Kelei Lian, Chunfeng Adeli, Ehsan Huo, Jing Shi, Yinghuan Gao, Yang Zhang, Bing Zhang, Junfeng Shen, Dinggang Medical School of Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing China Department of Radiology Nanjing Drum Tower Hospital Nanjing University Medical School China Department of Radiology BRIC University of North Carolina Chapel HillNC United States Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of Department of Psychiatry and Behavioral Sciences Department of Computer Science Stanford University CA United States

Fully convolutional networks (FCN), including U-Net and V-Net, are widely-used network architecture for semantic segmentation in recent studies. However, conventional FCNs are typically trained by the cross-entropy or dice loss, in which the relationships among voxels are neglected. This often results in non-smooth neighborhoods in the output segmentation map. This problem becomes more serious in CT prostate segmentation as CT images are usually of low tissue contrast. To address this problem, we propose a two-stage framework. The first stage quickly localizes the prostate region. Then, the second stage precisely segments the prostate by a multi-task FCN-based on the U-Net architecture. We introduce a novel online voxel-triplet learning module through metric learning and voxel feature embeddings in the multi-task network. The proposed network has two branches guided by two tasks: 1) a segmentation sub-network aiming to generate prostate segmentations, and 2) a triplet learning sub-network aiming to improve the quality of the learned feature space supervised by a mixed of triplet and pair-wise loss function. The triplet learning sub-network samples triplets from the inter-mediate heatmap. Unlike conventional deep triplet learning methods that generate triplets before the training phase, our proposed voxel-triplets are sampled in an online manner and operates in an end-to-end fashion via multi-task learning. To evaluate the proposed method, we implement comprehensive experiments on a CT image dataset consisting of 339 patients. The ablation studies show that our method can effectively learn more representative voxel-level features compared with the conventional FCN network. And the comparisons show that the proposed method outperforms the state-of-the-art methods by a large margin. Copyright © 2020, The Authors. All rights reserved.

关键词： Urology

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Face Analysis for Coronary Heart Disease Diagnosis 12

Face Analysis for Coronary Heart Disease Diagnosis

引用

12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

作者： Lin, Shisong Zhuang, Xiaodong Huang, Shiyun Liu, Yahui Shen, Linlin Liao, Xinxue Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Cardiology department First affiliated hospital of Sun Yat-sen University Guangzhou China NHC Key Laboratory of Assisted Circulation Sun Yat-sen University Guangzhou China Shenzhen National Medical Science and Technology Co. Ltd. Shenzhen China

ISBN: (纸本)9781728148526

CHD (Coronary Heart Disease) is one of the leading causes of cardiovascular disease deaths. Invasive coronary arteriography is one of the widely used approaches to diagnose CHD. However, the time cost and expenses for most of the diagnosis methodologies are high and some patients are reluctant to do such a diagnosis. In this paper, we aim to develop a novel low cost method to predict CHD. As suggested by the cardiologists, there are substantial differences between the facial images of patients with CHD and those of healthy subjects. In this paper, we conduct an automatic analysis of the texture features extracted from eight ROIs (Regions of Interests) of the face images. Based on the texture features, random forest and decision tree are used to predict whether the subject has a CHD, or not. The experimental results on a set of 1528 face images collected from 309 subjects suggest that, our approach achieved a promising, i.e. 72.73%, prediction accuracy. © 2019 IEEE.

关键词： Diagnosis

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Learning with noisy labels for sentence-level sentiment classification

arXiv

引用

arXiv 2019年

作者： Wang, Hao Liu, Bing Li, Chaozhuo Yang, Yan Li, Tianrui School of Information Science and Technology Southwest Jiaotong University Department of Computer Science University of Illinois at Chicago State Key Lab of Software Development Environment Beihang University

Deep neural networks (DNNs) can fit (or even over-fit) the training data very well. If a DNN model is trained using data with noisy labels and tested on data with clean labels, the model may perform poorly. This paper studies the problem of learning with noisy labels for sentence-level sentiment classification. We propose a novel DNN model called NETAB (as shorthand for convolutional neural NETworks with AB-networks) to handle noisy labels during training. NETAB consists of two convolutional neural networks, one with a noise transition layer for dealing with the input noisy labels and the other for predicting 'clean' labels. We train the two networks using their respective loss functions in a mutual reinforcement manner. Experimental results demonstrate the effectiveness of the proposed model. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Fact discovery from knowledge base via facet decomposition

arXiv

引用

arXiv 2019年

作者： Fu, Zihao Lin, Yankai Liu, Zhiyuan Lam, Wai Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Hong Kong Department of Computer Science and Technology State Key Lab on Intelligent Technology and Systems National Lab for Information Science and Technology Tsinghua University Beijing China

During the past few decades, knowledge bases (KBs) have experienced rapid growth. Nevertheless, most KBs still suffer from serious incompletion. Researchers proposed many tasks such as knowledge base completion and relation prediction to help build the representation of KBs. However, there are some issues unsettled towards enriching the KBs. Knowledge base completion and relation prediction assume that we know two elements of the fact triples and we are going to predict the missing one. This assumption is too restricted in practice and prevents it from discovering new facts directly. To address this issue, we propose a new task, namely, fact discovery from knowledge base. This task only requires that we know the head entity and the goal is to discover facts associated with the head entity. To tackle this new problem, we propose a novel framework that decomposes the discovery problem into several facet discovery components. We also propose a novel autoencoder based facet component to estimate some facets of the fact. Besides, we propose a feedback learning component to share the information between each facet. We evaluate our framework using a benchmark dataset and the experimental results show that our framework achieves promising results. We also conduct extensive analysis of our framework in discovering different kinds of facts. The source code of this paper can be obtained from https: //***/thunlp/FFD. Copyright © 2019, The Authors. All rights reserved.

关键词： Knowledge based systems

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OpenHowNet: An open sememe-based lexical knowledge base

arXiv

引用

arXiv 2019年

作者： Qi, Fanchao Yang, Chenghao Liu, Zhiyuan Dong, Qiang Sun, Maosong Dong, Zhendong Department of Computer Science and Technology Tsinghua University Institute for Artificial Intelligence Tsinghua University State Key Lab on Intelligent Technology and Systems Tsinghua University Software College Beihang University HowNet Technology Inc

In this paper, we present an open sememe-based lexical knowledge base OpenHowNet. Based on well-known HowNet, OpenHowNet comprises three components: core data which is composed of more than 100 thousand senses annotated with sememes, OpenHowNet Web which gives a brief introduction to OpenHowNet as well as provides online exhibition of OpenHowNet information, and OpenHowNet API which includes several useful APIs such as accessing OpenHowNet core data and drawing sememe tree structures of senses. In the main text, we first give some backgrounds including definition of sememe and details of HowNet. And then we introduce some previous HowNet and sememe-based research works. Last but not least, we detail the constituents of OpenHowNet and their basic features and functionalities. Additionally, we briefly make a summary and list some future works. Copyright © 2019, The Authors. All rights reserved.

关键词： Trees (mathematics)

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User review-based change file localization for mobile applications

arXiv

引用

arXiv 2019年

作者： Zhou, Yu Su, Yanqi Chen, Taolue Huang, Zhiqiu Gall, Harald Panichella, Sebastiano College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Information Systems Birkbeck University of London United Kingdom Department of Informatics University of Zurich Switzerland Zurich University of Applied Science Switzerland

In the current mobile app development, novel and emerging DevOps practices (e.g., Continuous Delivery, Integration, and user feedback analysis) and tools are becoming more widespread. For instance, the integration of user feedback (provided in the form of user reviews) in the software release cycle represents a valuable asset for the maintenance and evolution of mobile apps. To fully make use of these assets, it is highly desirable for developers to establish semantic links between the user reviews and the software artefacts to be changed (e.g., source code and documentation), and thus to localize the potential files to change for addressing the user feedback. In this paper, we propose RISING (Review Integration via claSsification, clusterIng, and linkiNG), an automated approach to support the continuous integration of user feedback via classification, clustering, and linking of user reviews. RISING leverages domain-specific constraint information and semi-supervised learning to group user reviews into multiple fine-grained clusters concerning similar users’ requests. Then, by combining the textual information from both commit messages and source code, it automatically localizes potential change files to accommodate the users’ requests. Our empirical studies demonstrate that the proposed approach outperforms the state-of-the-art baseline work in terms of clustering and localization accuracy, and thus produces more reliable results. Copyright © 2019, The Authors. All rights reserved.

关键词： Integration

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Meta mask correction for nuclei segmentation in histopathological image

arXiv

引用

arXiv 2021年

作者： Shi, Jiangbo Jia, Chang Gao, Zeyu Gong, Tieliang Wang, Chunbao Li, Chen National Engineering Lab for Big Data Analytics Xi'an Jiao Tong University Shaanxi Xi'an710049 China School of Computer Science and Technology Xi'an Jiao tong University Shaanxi Xi'an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech.R&D Xi'an Jiao tong University Shaanxi Xi'an710049 China Department of Pathology The First Affiliated Hospital of Xi'an Jiaotong University Shaanxi Xi'an710061 China

Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks using a small amount of clean meta-data. Then the corrected masks can be used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model in an end-to-end way. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. It even achieves comparable performance with the model training on supervised data in some noisy settings. © 2021, CC BY.

关键词： Deep learning

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Localized heat diffusion in topological thermal materials

arXiv

引用

arXiv 2021年

作者： Qi, Minghong Wang, Dong Cao, Pei-Chao Zhu, Xue-Feng Qiu, Cheng-Wei Chen, Hongsheng Li, Ying Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy Zhejiang University Zhejiang University Haining314400 China School of Physics Innovation Institute Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Various unusual behaviors of artificial materials are governed by their topological properties, among which the edge state at the boundary of a photonic or phononic lattice has been captivated as a popular notion. However, this remarkable bulk-boundary correspondence and the related phenomena are missing in thermal materials. One reason is that heat diffusion is described in a non-Hermitian framework because of its dissipative nature. The other is that the relevant temperature field is mostly composed of modes that extend over wide ranges, making it difficult to be rendered within the tight-binding theory as commonly employed in wave physics. Here, we overcome the above challenges and perform systematic studies on heat diffusion in thermal lattices. Based on a continuum model, we introduce a state vector to link the Zak phase with the existence of the edge state, and thereby analytically prove the thermal bulk-boundary correspondence. We experimentally demonstrate the predicted edge states with a topologically protected and localized heat dissipation capacity. Our finding sets up a solid foundation to explore the topology in novel heat transfer manipulations. © 2021, CC BY.

关键词： Topology

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Stochastic primal-dual algorithms with faster convergence than O(1/√t) for problems without bilinear structure

arXiv

引用

arXiv 2019年

作者： Yan, Yan Xu, Yi Lin, Qihang Zhang, Lijun Yang, Tianbao Department of Computer Science University of Iowa Iowa CityIA52242 United States Department of Management Sciences University of Iowa Iowa CityIA52242 United States National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

Previous studies on stochastic primal-dual algorithms for solving min-max problems with faster convergence heavily rely on the bilinear structure of the problem, which restricts their applicability to a narrowed range of problems. The main contribution of this paper is the design and analysis of new stochastic primal-dual algorithms that use a mixture of stochastic gradient updates and a logarithmic number of deterministic dual updates for solving a family of convex-concave problems with no bilinear structure assumed. Faster convergence rates than O(1/√T) with T being the number of stochastic gradient updates are established under some mild conditions of involved functions on the primal and the dual variable. For example, for a family of problems that enjoy a weak strong convexity in terms of the primal variable and has a strongly concave function of the dual variable, the convergence rate of the proposed algorithm is O(1/T). We also investigate the effectiveness of the proposed algorithms for learning robust models and empirical AUC maximization. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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