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SSRN 2024年

作者： Cheng, Jinpeng Yang, Hao Cao, Xiaoyu Wu, Qiang Zhang, Na Yan, Zhiyu Wang, Hongbin Huang, Linsheng Ma, Xinming Xiong, Shuping Yang, Guijun College of Agronomy Henan Agricultural University Zhengzhou450046 China Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs Information Technology Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing100097 China National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei230601 China Zhuhai Orbita Aerospace Science &Technology Co. Ltd Zhuhai519080 China Sinochem Modern Agriculture Co. Ltd. Beijing100031 China College of Geological Engineering and Geomatics Chang’an University Xi’an710054 China

Grasping the planting information of seed maize is utterly important for strengthening the macro-control of the seed industry market and ensuring the safe production of conventional maize. At present, the traditional ground survey is the main method for surveying the planting distribution of seed maize, which is inefficient and expensive. And in the existing remote sensing classification methods of seed maize, most of them use texture information, and seldom use hyperspectral information. Here, we studied the "Zhuhai-1" satellite hyperspectral data to construct the spectral and spatial features of seed maize identification, and then used the Support Vector Machine (SVM) classifier to prepare the planting pattern map of seed maize, and finally an efficient and economical identification model for seed maize based on hyperspectral satellite images was proposed. The classification uses a multi-layer mask method to classify seed corn and conventional corn in the scene of corn planting distribution. The spectral feature extraction method of the classification model compares the two methods of Class Means Matrix Clustering Feature (CMMCF) and PCA-LDA (Principal Component analysis -Linear Discriminant analysis), and the extraction of spatial features uses Multiscale Extended Morphological Profile method (MEMP). Then, four feature combinations of PCA-LDA, CMMCF, PCA-LDA-MEMP and CMMCF-MEMP were constructed for classification. The classification results use overall accuracy (OA), mapping accuracy (PA), and user accuracy (UA) to evaluate the model accuracy;Fisher discriminant ratio (FR) evaluates feature separability. The results of the model show that the FR value of the texture feature in the model is higher than that of the spectral feature, and the texture feature plays an important role in the extraction of seed maize. CMMCF-MEMP-SVM model was the best, OA reached 94.10%, PA and UA extracted from seed maize were 91.28% and 92.43%, respectively. In addition, the CMMCF-MEMP

关键词： Morphology

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Chirality-2 fermion induced Anti-Klein tunneling in 2D checkerboard lattice

arXiv

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arXiv 2024年

作者： Hua, Jiannan Wang, Z.F. Zhu, W. Chen, Weiwei Department of Physics School of Science Westlake University Zhejiang Hangzhou310024 China Institute of Natural Sciences Westlake Institute for Advanced Study Zhejiang Hangzhou310024 China Hefei National Research Center for Physical Sciences at the Microscale CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics Department of Physics University of Science and Technology of China Anhui Hefei230026 China Hefei National Laboratory University of Science and Technology of China Anhui Hefei230088 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province College of Science China Jiliang University Hangzhou310018 China

The quantum tunneling effect in the two-dimensional (2D) checkerboard lattice is investigated. By analyzing the pseudospin texture of the states in a 2D checkerboard lattice based on the low-energy effective Hamiltonian, we find that this system has a chiral symmetry with chirality equal to 2. Although compared to regular chiral fermions, its pseudospin orientation does not vary uniformly. This suggests that the perfect reflection chiral tunneling, also known as the anti-Klein tunneling, is expected to appear in checkerboard lattice as well. In order to verify the conjecture, we calculate the transmission probability and find that normally incident electron states can be perfectly reflected by the barrier with hole states inside, and vice versa. Furthermore, we also numerically calculate the tunneling conductance of the checkerboard nanotube using the recursive Green’s function method. The results show that a perfect on-off ratio can be achieved by confining the energy of the incident states within a certain range. It also suggests that, by tuning the barrier, the checkerboard nanotube is able to work as a perfect "band filter" or "tunneling field effect transistor", which transmits electrons selectively with respect to the pseudospin of the incident electrons. Copyright © 2024, The Authors. All rights reserved.

关键词： Nanotubes

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Emotion recognition in doctor-patient interactions from real-world clinical video database: Initial development of artificial empathy

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Computer Methods and Programs in Biomedicine 2023年 233卷 107480-107480页

作者： Huang, Chih-Wei Wu, Bethany C.Y. Nguyen, Phung Anh Wang, Hsiao-Han Kao, Chih-Chung Lee, Pei-Chen Rahmanti, Annisa Ristya Hsu, Jason C. Yang, Hsuan-Chia Li, Yu-Chuan Jack Taipei Medical University Taipei Taiwan Taipei Medical University Ringgold standard institution - Center for Simulation in Medical Education Taipei 116 Taiwan National Taiwan University Children and Family Research Center Sponsored by CTBC Charity Foundation Taipei Taiwan Clinical Data Center Office of Data Science Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University TMU Da'an Campus 15 F No. 172-1 Kee lung Road Section 2 Da-an District Taipei Taiwan Research Center of Big Data and Meta-analysis Wanfang Hospital Taipei Medical University Taipei Taiwan Department of Dermatology Wanfang Hospital Taipei Medical University Taiwan Department of Dermatology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Delta Research Center Taipei Taiwan Department of Health Policy and Management Faculty of Medicine Public Health and Nursing Universitas Gadjah Mada Yogyakarta Indonesia International PhD Program in Biotech and Healthcare Management College of Management Taipei Medical University Taipei Taiwan Research Center of Data Science on Healthcare Industry College of Management Taipei Medical University Taipei Taiwan

Background and objective: The promising use of artificial intelligence (AI) to emulate human empathy may help a physician engage with a more empathic doctor-patient relationship. This study demonstrates the application of artificial empathy based on facial emotion recognition to evaluate doctor-patient relationships in clinical practice. Methods: A prospective study used recorded video data of doctor-patient clinical encounters in dermatology outpatient clinics, Taipei Municipal Wanfang Hospital, and Taipei Medical University Hospital collected from March to December 2019. Two cameras recorded the facial expressions of four doctors and 348 adult patients during regular clinical practice. Facial emotion recognition was used to analyze the basic emotions of doctors and patients with a temporal resolution of 1 second. In addition, a physician-patient satisfaction questionnaire was administered after each clinical session, and two standard patients gave impartial feedback to avoid bias. Results: data from 326 clinical session videos showed that (1) Doctors expressed more emotions than patients (t [326] > = 2.998, p © 2023

关键词： Emotion Recognition

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COVID-19大流行期间流感活动呈“断崖式”下降——佩戴口罩、人员流动变化及SARS-CoV-2干扰的作用

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Engineering 2023年第2期21卷 195-202,M0008页

作者：韩莎莎张婷吕岩赖圣杰戴佩希郑建东杨维中周晓华冯录召 Beijing International Center for Mathematical Research Peking UniversityBeijing 100871China Harvard Medical School Harvard UniversityBostonMA 02115USA School of Population Medicine and Public Health Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100730China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijing 100871China WorldPop School of Geography and Environmental ScienceUniversity of SouthamptonSouthampton SO171BJUK Division for Infectious Diseases Chinese Center for Disease Control and PreventionBeijing 102206China Department of Epidemiology and Biostatistics School of Public HealthPeking UniversityBeijing 100871China National Engineering Laboratory of Big Data Analysis and Applied Technology Peking UniversityBeijing 100871China

一般情况下,每年冬季是季节性流感高发季节,但在当前2019冠状病毒病(COVID-19)大流行期间,全球季节性流感活动呈“断崖式”下降。为应对即将到来的流感季节,亟需弄清这种前所未有的流感低水平流行的原因。本文中,我们探索了一种国家特... 详细信息

一般情况下,每年冬季是季节性流感高发季节,但在当前2019冠状病毒病(COVID-19)大流行期间,全球季节性流感活动呈“断崖式”下降。为应对即将到来的流感季节,亟需弄清这种前所未有的流感低水平流行的原因。本文中,我们探索了一种国家特异性推理模型,利用该模型估计中国、英国和美国这三个国家中佩戴口罩、人员流动变化(国际和国内)以及严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)干扰的影响。结果发现,在这些地区增加一周佩戴口罩的干预措施,流感活动可减少11.3%~35.2%。实施一周的限制人员流动措施对国际(1.7%~6.5%)和国内社区(1.6%~2.8%)的影响较小。2020年至2021年,仅佩戴口罩这一项干预措施就可使阳性率下降13.3%至19.8%。仅人员流动变化可使阳性率降低5.2%至14.0%,其中79.8%~98.2%归因于国际旅行限制。SARS-CoV-2干扰仅在2019年至2020年具有统计学显著效应。中国北方地区及英国的阳性百分比分别降低7.6%(2.4~14.4)和10.2%(7.2~13.6)。探索所得出的结果对了解非药物干预措施和其他呼吸系统疾病背景下的流感演变有一定意义,有助于制定卫生政策,并可为公共卫生措施的个性化设计提供信息。

关键词：卫生政策非药物干预呼吸系统疾病流行期间季节性流感流动变化个性化设计模型估计

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Two-Stream Regression Network for Dental Implant Position Prediction

arXiv

引用

arXiv 2023年

作者： Yang, Xinquan Li, Xuguang Li, Xuechen Chen, Wenting Shen, Linlin Li, Xin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China Department of Electrical Engineering City University of Hong Kong Hong Kong

In implant prosthesis treatment, the design of surgical guide requires lots of manual labors and is prone to subjective variations. When deep learning based methods has started to be applied to address this problem, the space between teeth are various and some of them might present similar texture characteristic with the actual implant region. Both problems make a big challenge for the implant position prediction. In this paper, we develop a two-stream implant position regression framework (TSIPR), which consists of an implant region detector (IRD) and a multi-scale patch embedding regression network (MSPENet), to address this issue. For the training of IRD, we extend the original annotation to provide additional supervisory information, which contains much more rich characteristic and do not introduce extra labeling costs. A multi-scale patch embedding module is designed for the MSPENet to adaptively extract features from the images with various tooth spacing. The global-local feature interaction block is designed to build the encoder of MSPENet, which combines the transformer and convolution for enriched feature representation. During inference, the RoI mask extracted from the IRD is used to refine the prediction results of the MSPENet. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TSIPR achieves superior performance than existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Interpretable Estimation of the Risk of Heart Failure Hospitalization from a 30-Second Electrocardiogram

Interpretable Estimation of the Risk of Heart Failure Hospit...

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E-Health and Bioengineering Conference (EHB)

作者： Sergio González Wan-Ting Hsieh Davide Burba Trista Pei-Chun Chen Chun-Li Wang Victor Chien-Chia Wu Shang-Hung Chang AI Center Inventec Corporation Taipei Taiwan Department of Internal Medicine Cardiovascular Division Chang Gung Memorial Hospital Linkou Taiwan College of Medicine Chang Gung University Taoyuan Taiwan Center for Big Data Analysis and Statistics Chang Gung Memorial Hospital Linkou Taiwan Graduate Institute of Nursing Chang Gung University of Science and Technology Taoyuan Taiwan

ISBN: (数字)9781665485579

ISBN: (纸本)9781665485586

Survival modeling in healthcare relies on explainable statistical models; yet, their underlying assumptions are often simplistic and, thus, unrealistic. Machine learning models can estimate more complex relationships and lead to more accurate predictions, but are non-interpretable. This study shows it is possible to estimate hospitalization for congestive heart failure by a 30 seconds single-lead electrocardiogram signal. Using a machine learning approach not only results in greater predictive power but also provides clinically meaningful interpretations. We train an eXtreme Gradient Boosting accelerated failure time model and exploit SHapley Additive exPlanations values to explain the effect of each feature on predictions. Our model achieved a concordance index of 0.828 and an area under the curve of 0.853 at one year and 0.858 at two years on a held-out test set of 6,573 patients. These results show that a rapid test based on an electrocardiogram could be crucial in targeting and treating high-risk individuals.

关键词： Biological system modeling Estimation Medical services Predictive models Electrocardiography Feature extraction Boosting

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Leveraging Probe data and Machine Learning to Derive and Interpret Macroscopic Fundamental Diagrams Across U.S. Cities

Leveraging Probe Data and Machine Learning to Derive and Int...

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IEEE International Conference on Big data

作者： Ling Jin Xiaodan Xu Yuhan Wang Kaveh Farokhi Sadabadi Alina Lazar Duleep Rathgamage Don Zachary Needell C Anna Spurlock Mahyar Amirgholy Mona Asudegi Energy Technology Area Berkeley National Laboratory Berkeley CA Center for Advanced Transportation Technology University of Maryland College Park MD Department of Computer Science and Information Youngstown State University Youngstown OH School of Data Science and Analytics Kennesaw State University Marietta GA Energy Analysis and Environmental Impacts Division Lawrence Berkeley National Laboratory Berkeley CA Civil and Environmental Engineering Department Kennesaw State University Marietta GA Office of Transportation Policy Studies Federal Highway Administration Washington DC D.C.

Macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. Understanding network-wide traffic through MFDs can optimally allocate demand to existing networks, improving performance by maximizing network production and avoiding congestion. However, due to historical data limitations, empirically derived MFD models are sparse in the literature, especially for the U.S. cities. Leveraging a large-scale and granular census-tract-level flow and density derived from vehicle probe data, this research is the first to develop a machine learning approach to both derive MFD models and interpret their underlying difference among urban networks across the entire United States. Among the four machine learning methods tested here XGBoost is found to deliver the best performance to predict the network traffic flow for given vehicular density and location attributes. Interaction Shapley Additive explanation (SHAP) values are used to interpret the factors, such as land use, transportation infrastructure, and network topology, that influence the flow-density relationships among locations. The analysis framework developed in this work can generate datadriven MFDs and a deeper understanding of their shape dependence on network, infrastructure, and land use characteristics, which can be used by transportation authorities to derive and optimize location-specific MFDs facilitating more informed management and planning decisions at the network level.

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Interpretable estimation of the risk of heart failure hospitalization from a 30-second electrocardiogram

arXiv

引用

arXiv 2022年

作者： González, Sergio Hsieh, Wan-Ting Burba, Davide Chen, Trista Pei-Chun Wang, Chun-Li Wu, Victor Chien-Chia Chang, Shang-Hung AI Center Inventec Corporation Taipei Taiwan Cardiovascular Division Department of Internal Medicine Chang Gung Memorial Hospital Linkou Taiwan College of Medicine Chang Gung University Taoyuan Taiwan Center for Big Data Analysis and Statistics Chang Gung Memorial Hospital Linkou Taiwan Graduate Institute of Nursing Chang Gung University of Science and Technology Taoyuan Taiwan

Survival modeling in healthcare relies on explainable statistical models;yet, their underlying assumptions are often simplistic and, thus, unrealistic. Machine learning models can estimate more complex relationships and lead to more accurate predictions, but are non-interpretable. This study shows it is possible to estimate hospitalization for congestive heart failure by a 30 seconds single-lead electrocardiogram signal. Using a machine learning approach not only results in greater predictive power but also provides clinically meaningful interpretations. We train an eXtreme Gradient Boosting accelerated failure time model and exploit SHapley Additive exPlanations values to explain the effect of each feature on predictions. Our model achieved a concordance index of 0.828 and an area under the curve of 0.853 at one year and 0.858 at two years on a held-out test set of 6,573 patients. These results show that a rapid test based on an electrocardiogram could be crucial in targeting and treating high-risk individuals. © 2022, CC BY.

关键词： Electrocardiograms

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AdaFuse: Adaptive Medical Image Fusion Based on Spatial-Frequential Cross Attention

arXiv

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arXiv 2023年

作者： Gu, Xianming Wang, Lihui Deng, Zeyu Cao, Ying Huang, Xingyu Zhu, Yue-Min Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 Lyon69621 France

Multi-modal medical image fusion is essential for the precise clinical diagnosis and surgical navigation since it can merge the complementary information in multi-modalities into a single image. The quality of the fused image depends on the extracted single modality features as well as the fusion rules for multi-modal information. Existing deep learning-based fusion methods can fully exploit the semantic features of each modality, they cannot distinguish the effective low and high frequency information of each modality and fuse them adaptively. To address this issue, we propose AdaFuse, in which multimodal image information is fused adaptively through frequency-guided attention mechanism based on Fourier transform. Specifically, we propose the cross-attention fusion (CAF) block, which adaptively fuses features of two modalities in the spatial and frequency domains by exchanging key and query values, and then calculates the cross-attention scores between the spatial and frequency features to further guide the spatial-frequential information fusion. The CAF block enhances the high-frequency features of the different modalities so that the details in the fused images can be retained. Moreover, we design a novel loss function composed of structure loss and content loss to preserve both low and high frequency information. Extensive comparison experiments on several datasets demonstrate that the proposed method outperforms state-of-the-art methods in terms of both visual quality and quantitative metrics. The ablation experiments also validate the effectiveness of the proposed loss and fusion strategy. © 2023, CC BY.

关键词： Deep learning

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Touchstone Benchmark: Are We on the Right Way for Evaluating AI algorithms for Medical Segmentation?

arXiv

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arXiv 2024年

作者： Bassi, Pedro R.A.S. Li, Wenxuan Tang, Yucheng Isensee, Fabian Wang, Zifu Chen, Jieneng Chou, Yu-Cheng Roy, Saikat Kirchhoff, Yannick Rokuss, Maximilian Huang, Ziyan Ye, Jin He, Junjun Wald, Tassilo Ulrich, Constantin Baumgartner, Michael Maier-Hein, Klaus H. Jaeger, Paul Ye, Yiwen Xie, Yutong Zhang, Jianpeng Chen, Ziyang Xia, Yong Xing, Zhaohu Zhu, Lei Sadegheih, Yousef Bozorgpour, Afshin Kumari, Pratibha Azad, Reza Merhof, Dorit Shi, Pengcheng Ma, Ting Du, Yuxin Bai, Fan Huang, Tiejun Zhao, Bo Wang, Haonan Li, Xiaomeng Gu, Hanxue Dong, Haoyu Yang, Jichen Mazurowski, Maciej A. Gupta, Saumya Wu, Linshan Zhuang, Jiaxin Chen, Hao Roth, Holger Xu, Daguang Blaschko, Matthew B. Decherchi, Sergio Cavalli, Andrea Yuille, Alan L. Zhou, Zongwei Department of Computer Science Johns Hopkins University United States Department of Pharmacy and Biotechnology University of Bologna Italy Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Italy NVIDIA United States Germany Germany ESAT-PSI KU Leuven Belgium Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany Shanghai Jiao Tong University China Shanghai Artificial Intelligence Laboratory China Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany DKFZ Germany School of Computer Science and Engineering Northwestern Polytechnical University China Australian Institute for Machine Learning The University of Adelaide Australia College of Computer Science and Technology Zhejiang University China Hong Kong University of Science and Technology Guangzhou China Hong Kong University of Science and Technology Hong Kong Faculty of Informatics and Data Science University of Regensburg Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Fraunhofer Institute for Digital Medicine MEVIS Germany Electronic & Information Engineering School Harbin Institute of Technology Shenzhen China China The Chinese University of Hong Kong Hong Kong Peking University China Department of Electrical and Computer Engineering Duke University United States Stony Brook University United States Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science Hong Kong University of Science and Technology Hong Kong Data Science and Computation Facility Fondazione Istituto Italiano di Tecnologia Italy Ecole Polytechnique Fédérale de Lausanne Switzerland

How can we test AI performance? This question seems trivial, but it isn’t. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain. Copyright © 2024, The Authors. All rights reserved.

关键词： Open systems

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