检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Gao, Wanling Huang, Yunyou Cui, Dandan Yu, Zhuoming Liu, Wenjing Liang, Xiaoshuang Zhao, Jiahui Xie, Jiyue Li, Hao Ma, Li Ye, Ning Kang, Yumiao Luo, Dingfeng Pan, Peng Huang, Wei Liu, Zhongmou Hu, Jizhong Zhao, Gangyuan Jiang, Chongrong Huang, Fan Wei, Tianyi Tang, Suqin Xia, Bingjie Zhang, Zhifei Zhan, Jianfeng Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China International Open Benchmark Council China University of Chinese Academy of Sciences Beijing100086 China Guilin Medical University Guilin541100 China Affiliated Hospital of Guilin Medical University Guilin541000 China XuanJi Technology Co. Ltd. Guilin541000 China Xing An County People’s Hospital Guilin541300 China Meng Shan County People’s Hospital Wuzhou543000 China Guilin People’s Hospital Guilin541000 China Yong Fu County People’s Hospital Guilin541000 China Ling Chuan County People’s Hospital Guilin541000 China Quan Zhou County People’s Hospital Guilin541000 China Guan Yang County People’s Hospital Guilin541000 China The Second Affiliated Hospital of Guilin Medical University Guilin541000 China International College Guangxi University Nanning530004 China Capital Medical University Beijing100069 China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China

A profound gap persists between artificial intelligence (AI) and clinical practice in medicine, primarily due to the lack of rigorous and cost-effective evaluation methodologies. State-of-the-art and state-of-the-practice AI model evaluations are limited to lab.ratory studies on medical datasets or direct clinical trials with no or solely patient-centered controls. Moreover, the crucial role of clinicians in collab.rating with AI, pivotal for determining its impact on clinical practice, is often overlooked. For the first time, we emphasize the critical necessity for rigorous and cost-effective evaluation methodologies for AI models in clinical practice, featuring patient/clinician-centered (dual-centered) AI randomized controlled trials (DC-AI RCTs) and virtual clinician-based in-silico trials (VC-MedAI) as an effective proxy for DC-AI RCTs. Leveraging 7500 diagnosis records from two-phase inaugural DC-AI RCTs across 14 medical centers with 125 clinicians, our results demonstrate the necessity of DC-AI RCTs and the effectiveness of VC-MedAI. Notably, VC-MedAI performs comparably to human clinicians, replicating insights and conclusions from prospective DC-AI RCTs. We envision DC-AI RCTs and VC-MedAI as pivotal advancements, presenting innovative and transformative evaluation methodologies for AI models in clinical practice, offering a preclinical-like setting mirroring conventional medicine, and reshaping development paradigms in a cost-effective and fast-iterative manner. Chinese Clinical Trial Registration: ChiCTR2400086816. © 2024, CC BY-NC-ND.

关键词： Artificial intelligence

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Motif Discovery via Convolutional Networks with K-mer Embedding 15th

Motif Discovery via Convolutional Networks with K-mer Embedd...

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15th International Conference on intelligent computing, ICIC 2019

作者： Wang, Dailun Zhang, Qinhu Yuan, Chang-An Qin, Xiao Huang, Zhi-Kai Shang, Li Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Science Computing and Intelligent Information Processing of GuangXi Higher Education Key Laboratory Nanning Normal University NanningGuangxi China College of Mechanical and Electrical Engineering Nanchang Institute of Technology NanchangJiangxi330099 China Department of Communication Technology College of Electronic Information Engineering Suzhou Vocational University SuzhouJiangsu215104 China

ISBN: (纸本)9783030269685

With the rapid development of deep learning, some discriminative motif discovery methods based on deep neural network are gradually becoming the mainstream, which also bringing huge improvement of prediction accuracy. In this paper, we propose a convolutional neural network based architecture (eCNN), combining embedding layer with GloVe. Firstly, eCNN divides each single sequence of ChIP-seq datasets into multiple subsequences called k-mers by a sliding window, and then encoding k-mers into a relatively low dimension vectors by GloVe, and finally scores each vector using multiple convolutional networks. The experiment shows that our architecture can get good results on the task of motif discovery. © 2019, Springer Nature Switzerland AG.

关键词： Embeddings

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Rotation differential invariants of images generated by two fundamental differential operators

arXiv

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arXiv 2019年

作者： Mo, Hanlin Li, Hua Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

In this paper, we design two fundamental differential operators for the derivation of rotation differential invariants of images. Each differential invariant obtained by using the new method can be expressed as a homogeneous polynomial of image partial derivatives, which preserve their values when the image is rotated by arbitrary angles. We produce all possible instances of homogeneous invariants up to the given order and degree, and discuss the independence of them in detail. As far as we know, no previous papers have published so many explicit forms of high-order rotation differential invariants of images. In the experimental part, texture classification and image patch verification are carried out on popular real databases. These rotation differential invariants are used as image feature vector. We mainly evaluate the effects of various factors on the performance of them. The experimental results also validate that they have better performance than some commonly used image features in some cases. Copyright © 2019, The Authors. All rights reserved.

关键词： Rotation

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Spectral-Spatial Weighted Sparse Nonnegative Tensor Factorization for Hyperspectral Unmixing

Spectral-Spatial Weighted Sparse Nonnegative Tensor Factoriz...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Shaoquan Zhang Guorong Zhang Chengzhi Deng Jun Li Shengqian Wang Jun Wang Antonio Plaza Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang China Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and Planning Sun Yat-sen University Guangzhou China Hyperspectral Computing Laboratory Escuela Politécnica University of Extremadura Cáceres Spain

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Hyperspectral unmixing aims to decompose a hyperspectral image (HSI) into a collection of constituent materials, or end-members, and their corresponding abundance fractions. Recently, nonnegative tensor factorization (NTF)-based spectral unmixing methods have attracted significant attention owing to their outstanding performance when representing an HSI without any information loss. However, tensor factorization-based HSI methods do not fully exploit the spatial contextual information present in the scene. Besides, these approaches are sensitive to low signal-to-noise ratio (SNR) in HSIs. To address this limitation, we propose a new spectral-spatial weighted sparse nonnegative tensor factorization (SSWNTF) method to preserve the spatial details in the abundance maps via the spectral and spatial weighting factors. Our experiments with simulated data sets certified that the proposed method outperforms other advanced methods.

关键词： Hyperspectral imaging Tensors Signal to noise ratio Signal processing algorithms Matrix decomposition Sparse matrices Noise measurement

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Hierarchical Attention Network for Predicting DNA-Protein Binding Sites 15th

Hierarchical Attention Network for Predicting DNA-Protein Bi...

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15th International Conference on intelligent computing, ICIC 2019

作者： Yu, Wenbo Yuan, Chang-An Qin, Xiao Huang, Zhi-Kai Shang, Li Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Science Computing and Intelligent Information Processing of GuangXi Higher Education Key Laboratory Nanning Normal University NanningGuangxi China College of Mechanical and Electrical Engineering Nanchang Institute of Technology NanchangJiangxi330099 China Department of Communication Technology College of Electronic Information Engineering Suzhou Vocational University SuzhouJiangsu215104 China

ISBN: (纸本)9783030269685

Discovering DNA-protein binding sites, also known as motif discovery, is the foundation for further analyses of transcription factors (TFs). Deep learning algorithms such as convolutional neural networks (CNN) and recurrent neural networks (RNN) are introduced to motif discovery task and have achieved state-of–art performance. However, these methods still have limitations such as neglecting the context information in large-scale sequencing data. Thus, inspired by the similarity between DNA sequence and human language, in this paper we propose a hierarchical attention network for predicting DNA-protein binding sites which is based on a natural language processing method for document classification. The proposed method is tested on real ChIP-seq datasets and the experimental results show a considerable improvement compared with two well-tested deep learning-based sequence model, DeepBind and Deepsea. © 2019, Springer Nature Switzerland AG.

关键词： Transcription factors

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***: Unveiling Intricate Interactions Between AI and Clinicians through an Open-Access Database

arXiv

引用

arXiv 2024年

作者： Gao, Wanling Liu, Yuan Yu, Zhuoming Cui, Dandan Liu, Wenjing Liang, Xiaoshuang Zhao, Jiahui Xie, Jiyue Li, Hao Ma, Li Ye, Ning Kang, Yumiao Luo, Dingfeng Pan, Peng Huang, Wei Liu, Zhongmou Hu, Jizhong Huang, Fan Zhao, Gangyuan Jiang, Chongrong Wei, Tianyi Zhang, Zhifei Huang, Yunyou Zhan, Jianfeng Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China International Open Benchmark Council University of Chinese Academy of Sciences Beijing100086 China Guilin Medical University Guilin541100 China Affiliated Hospital of Guilin Medical University Guilin541000 China Xing An County People’s Hospital Guilin541300 China Meng Shan County People’s Hospital Wuzhou543000 China XuanJi Technology Co. Ltd. Guilin541000 China Guilin People’s Hospital Guilin541000 China Yong Fu County People’s Hospital Guilin541000 China Ling Chuan County People’s Hospital Guilin541000 China The Second Affiliated Hospital of Guilin Medical University Guilin541000 China Quan Zhou County People’s Hospital Guilin541000 China Guan Yang County People’s Hospital Guilin541000 China International College Guangxi University Nanning530004 China Capital Medical University Beijing100069 China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China

Artificial Intelligence (AI) plays a crucial role in medical field and has the potential to revolutionize healthcare practices. However, the success of AI models and their impacts hinge on the synergy between AI and medical specialists, with clinicians assuming a dominant role. Unfortunately, the intricate dynamics and interactions between AI and clinicians remain undiscovered and thus hinder AI from being translated into medical practice. To address this gap, we have curated a groundbreaking database called ***. This database is the first of its kind for studying the interactions between AI and clinicians. It derives from 7,500 collab.rative diagnosis records on a life-threatening medical emergency – Sepsis – from 14 medical centers across China. For the patient cohorts well-chosen from MIMIC databases, the AI-related information comprises the model property, feature input, diagnosis decision, and inferred probabilities of sepsis onset presently and within next three hours. The clinician-related information includes the viewed examination data and sequence, viewed time, preliminary and final diagnosis decisions with or without AI assistance, and recommended treatment. © 2024, CC BY-NC-ND.

关键词： Database systems

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An acupoint-originated human interstitial fluid circulatory network

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Chinese Medical Journal 2021年第19期134卷 2365-2369页

作者： Hong-Yi Li Fang Wang Min Chen Zhi-Jian Zheng Ya-Jun Yin Jun Hu Hua Li Andreas Sammer Georg Feigl Norbert Maurer Chao Ma Fu-Sui Ji Department of Cardiology Beijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical SciencesBeijing 100730China Department of Radiology Beijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical SciencesBeijing 100730China Department of Accupuncture Beijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical SciencesBeijing 100730China Department of Engineering Mechanics Tsinghua UniversityBeijing 10084China Key Lab of Interfacial Physics and Technology Shanghai Institute of Applied PhysicsChinese Academy of SciencesShanghai 201210China Shanghai Synchrotron Radiation Facility Shanghai Advanced Research InstituteChinese Academy of SciencesShanghai 201210China Key Lab of Intelligent Information Processing Institute of Computing Technology and University of Chinese Academy of SciencesBeijing 100190China Ordination Dr.Sammer Dr.med.univ.Andreas SammerGrazAustria Institute of Anatomy and Clinical Morphology University of Witten/HerdeckeWittenGermany University Clinic for Physical Medicine Rehabilitation and Occupational MedicineMedical University of ViennaViennaAustria Institute of Basic Medical Sciences Chinese Academy of Medical SciencesDepartment of Human AnatomyHistology and EmbryologySchool of Basic MedicinePeking Union Medical CollegeBeijing 100005China Chinese Institute for Brain Research Beijing 100005China

To the Editor:Since 1027,when the Tian Sheng Bronze Statue showing acupoints and meridians was first introduced for medical practitioners,it has evolved to the currently used acupoints and meridians atlas in traditional Chinese medicine(TCM).[1]To date,the acupoints represent defined areas on the body surface relative to certain *** acupoints in the extremity endings would connect anatomically with diverse associated visceral organs or tissues,forming a Meridian and Collateral *** on the atlas,each main meridian is usually a virtual line connecting a group of adjacent acupoints that correlate with certain visceral *** modern medical science,the anatomical structures of main meridians remain largely unclarified.

关键词： organs originated connecting

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RGB-T image saliency detection via collab.rative graph learning

arXiv

引用

arXiv 2019年

作者： Tu, Zhengzheng Xia, Tian Li, Chenglong Wang, Xiaoxiao Ma, Yan Tang, Jin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Institute of Physical Science and Information Technology Anhui University Hefei China

Image saliency detection is an active research topic in the community of computer vision and multimedia. Fusing complementary RGB and thermal infrared data has been proven to be effective for image saliency detection. In this paper, we propose an effective approach for RGB-T image saliency detection. Our approach relies on a novel collab.rative graph learning algorithm. In particular, we take superpixels as graph nodes, and collab.ratively use hierarchical deep features to jointly learn graph affinity and node saliency in a unified optimization framework. Moreover, we contribute a more challenging dataset for the purpose of RGB-T image saliency detection, which contains 1000 spatially aligned RGB-T image pairs and their ground truth annotations. Extensive experiments on the public dataset and the newly created dataset suggest that the proposed approach performs favorably against the state-of-the-art RGB-T saliency detection methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Fair and autonomous sharing of federate learning models in mobile Internet of Things

arXiv

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arXiv 2020年

作者： Hao, Xiaohan Ren, Wei Xiong, Ruoting Zheng, Xianghan Zhu, Tianqing Xiong, Neal N. School of Computer Science China University of Geoscience Wuhan China Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Mingbyte Technology QingDao China School of Software University of Technology Sydney UltimoNSW2007 Australia Dept. of Mathematics and Computer Science Northeastern State University United States

Federate learning can conduct machine learning as well as protect the privacy of self-owned training data on corresponding ends, instead of having to upload to a central trusted data aggregation server. In mobile scenarios, a centralized trusted server may not be existing, and even though it exists, the delay will not be manageable, e.g., smart driving cars. Thus, mobile federate learning at the edge with privacy-awareness is attracted more and more attentions. It then imposes a problem - after data are trained on a mobile terminal to obtain a learned model, how to share the model parameters among others to create more accurate and robust accumulative final model. This kind of model sharing confronts several challenges, e.g., the sharing must be conducted without a third trusted party (autonomously), and the sharing must be fair as model training (by training data)is valuable. To tackle the above challenges, we propose a smart contract and IPFS (Inter-Planetary File System) based model sharing protocol and algorithms to address the challenges. The proposed protocol does not rely on a trusted third party, where individual-learned models are shared/stored in corresponding ends. Conducted through extensive experiments, three main steps of the proposed protocol are evaluated. The average executive time of the three steps are 0.059s, 0.060s and 0.032s, demonstrating its efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Smart contract

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ADAM CHALLENGE: DETECTING AGE-RELATED MACULAR DEGENERATION FROM FUNDUS IMAGES

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Fu, Huazhu Sun, Xu Cao, Xingxing Lin, Fengbin Son, Jaemin Kim, Sunho Quellec, Gwenole Matta, Sarah Shankaranarayana, Sharath M. Chen, Yi-Ting Wang, Chuen-Heng Shah, Nisarg A. Lee, Chia-Yen Hsu, Chih-Chung Xie, Hai Lei, Baiying Baid, Ujjwal Innani, Shubham Dang, Kang Shi, Wenxiu Kamble, Ravi Singhal, Nitin Wang, Ching-Wei Lo, Shih-Chang Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The ADAM Challenge State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore CONICET Yatiris lab of Pladema Institute Tandil Argentina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Inserm UMR 1101 BrestF-29200 France Univ Bretagne Occidentale BrestF-29200 France Zasti India Pvt. Ltd. India Graphen.Inc Muen Biomedical and Optoelectronics Technologies Inc Taiwan Department of Electrical Engineering Indian Institute of Technology Rajasthan Jodhpur India Department of Electrical Engineering National United University Taiwan Institute of Data Science Department of Statistics National Cheng Kung University Taiwan Shenzhen University Shenzhen China SGGS Institute of Engineering and Technology Nanded India Suzhou Tisu Information Technology Co. Ltd China AIRA Matrix Mumbai India Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taiwan

Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss caused by this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM), which was held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. As part of the ADAM challenge, we have released a comprehensive dataset of 1200 fundus images with AMD diagnostic lab.ls, pixel-wise segmentation masks for both optic disc and AMD-related lesions (drusen, exudates, hemorrhages and scars, among others), as well as the coordinates corresponding to the location of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models using this dataset. During the ADAM challenge, 610 results were submitted for online evaluation, with 11 teams finally participating in the onsite challenge. This paper introduces the challenge, the dataset and the evaluation methods, as well as summarizes the participating methods and analyzes their results for each task. In particular, we observed that the ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models. © 2022, CC BY-NC-ND.

关键词： Cost effectiveness

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