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arXiv 2023年

作者： Zhou, Yirong Wu, Yanhuang Su, Yuhan Li, Jing Cai, Jianyun You, Yongfu Guo, Di Qu, Xiaobo The Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361104 China The Department of Electronic Science Key Laboratory of Digital Fujian on IoT Communication Architecture and Safety Technology Xiamen University Xiamen361104 China Shanghai Electric Group CO. LTD Shanghai200001 China China Telecom Group Quanzhou362000 China China Mobile Group Xiamen361005 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Magnetic Resonance Imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure. Additionally, local data processing demands substantial manpower and hardware investments. Data isolation across different healthcare institutions hinders cross-institutional collaboration in clinics and research. In this work, we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing, 6G bandwidth, edge computing, federated learning, and blockchain technology. This system is called Cloud-MRI, aiming at solving the problems of MRI data storage security, transmission speed, AI algorithm maintenance, hardware upgrading, and collaborative work. The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data (ISMRMRD) format. Then, the data are uploaded to the cloud or edge nodes for fast image reconstruction, neural network training, and automatic analysis. Then, the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services. The Cloud-MRI system will save the raw imaging data, reduce the risk of data loss, facilitate inter-institutional medical collaboration, and finally improve diagnostic accuracy and work efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Hagnifinder: Recovering magnification information of digital histological images using deep learning

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Journal of Pathology Informatics 2023年 14卷 100302-100302页

作者： Zhang, Hongtai Liu, Zaiyi Song, Mingli Lu, Cheng School of Computer and Cyber Sciences Communication University of China Beijing 100024 China Department of Radiology Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou 510080 China Medical Research Institute Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou 510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou 510080 China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing 100024 China

Background and objective: Training a robust cancer diagnostic or prognostic artificial intelligent model using histology images requires a large number of representative cases with labels or annotations, which are difficult to obtain. The histology snapshots available in published papers or case reports can be used to enrich the training dataset. However, the magnifications of these invaluable snapshots are generally unknown, which limits their usage. Therefore, a robust magnification predictor is required for utilizing those diverse snapshot repositories consisting of different diseases. This paper presents a magnification prediction model named Hagnifinder for H&E-stained histological images. Methods: Hagnifinder is a regression model based on a modified convolutional neural network (CNN) that contains 3 modules: Feature Extraction Module, Regression Module, and Adaptive Scaling Module (ASM). In the training phase, the Feature Extraction Module first extracts the image features. Secondly, the ASM is proposed to address the learned feature values uneven distribution problem. Finally, the Regression Module estimates the mapping between the regularized extracted features and the magnifications. We construct a new dataset for training a robust model, named Hagni40, consisting of 94 643 H&E-stained histology image patches at 40 different magnifications of 13 types of cancer based on The Cancer Genome Atlas. To verify the performance of the Hagnifinder, we measure the accuracy of the predictions by setting the maximum allowable difference values (0.5, 1, and 5) between the predicted magnification and the actual magnification. We compare Hagnifinder with state-of-the-art methods on a public dataset BreakHis and the Hagni40. Results: The Hagnifinder provides consistent prediction accuracy, with a mean accuracy of 98.9%, across 40 different magnifications and 13 different cancer types when Resnet50 is used as the feature extractor. Compared with the state-of-the-art method

关键词： Deep learning Histology images Magnification prediction Regression model

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Coherent Interactions of Free Electrons and Matter: Toward Tunable Compact X-ray Sources

arXiv

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arXiv 2024年

作者： Balanov, Amnon Gorlach, Alexey Baryshevsky, Vladimir Feranchuk, Ilya Nitta, Hideo Hayakawa, Yasushi Shchagin, Alexander Takabayashi, Yuichi Danon, Yaron Wong, Liang Jie Kaminer, Ido Department of Electrical and Computer Engineering Technion Israel Institute of Technology Haifa3200003 Israel Research Institute for Nuclear Problems Belarusian State University Bobruiskaya Str. 11 Minsk220030 Belarus Physics Department Belarusian State University Nezavisimosti Ave 4 Minsk220030 Belarus Department of Physics Tokyo Gakugei University Koganei Tokyo184 Japan Laboratory for Electron Beam Research and Application Nihon University Narashinodai 7-24-1 Funabashi274-8501 Japan Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany Kharkiv Institute of Physics and Technology Academicheskaya 1 Kharkiv61108 Ukraine SAGA Light Source 8-7 Yayoigaoka Tosu Saga841-0005 Japan Department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute TroyNY12180 United States School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Ave Singapore639798 Singapore

Compact laboratory-scale X-ray sources still rely on the same fundamental principles as in the first X-ray tubes developed more than a century ago. In recent years, significant research and development have focused on large-scale X-ray sources such as synchrotrons and free-electron lasers, leading to the generation of high-brightness coherent X-rays. However, the large size and high costs of such sources prevent their widespread use. The quest for a compact and coherent X-ray source has long been a critical objective in modern physics, gaining further importance in recent years for industrial applications and fundamental scientific research. Here, we review the physical mechanisms governing compact coherent X-ray generation. Of current interest are coherent periodic interactions of free electrons in crystalline materials, creating hard X-rays via a mechanism known as parametric X-ray radiation (PXR). Over the past decade, X-ray sources leveraging this mechanism have demonstrated state-of-the-art tunability, directionality, and broad spatial coherence, enabling X-ray phase-contrast imaging on a compact scale. The coming years are expected to show substantial miniaturization of compact X-ray sources, facilitated by progress in electron beam technologies. This review compares the most promising mechanisms used for hard-X-ray generation, contrasting parametric X-ray radiation with inverse Compton scattering and characteristic radiation from a liquid-jet anode. We cover the most recent advancements, including the development of new materials, innovative geometrical designs, and specialized optimization techniques, aiming toward X-ray flux levels suitable for medical imaging and X-ray spectroscopy in compact scales. Copyright © 2024, The Authors. All rights reserved.

关键词： X ray tubes

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In-Depth Evaluation of Nutritive, Chemical Constituents and Anti-Glycemic Properties of Jackfruit (Artocarpus Heterophyllus Lam) Clonal Accessions with Flake Colour Diversity from Eastern Sub-Himalayan Plains of India

SSRN

引用

SSRN 2022年

作者： Natta, Suman Pal, Kumaresh Alam, Bharath Kumar Mondal, Debayan Dutta, Sudip Kumar Sahana, Nandita Mandal, Somnath Bhowmick, Nilesh Das, Soumitra Sankar Mondal, Prithusayak Pandit, Goutam Kumar Pal, Prodyut Kumar Choudhury, Ashok Department of Biochemistry Uttar Banga Krishi Viswavidyalaya Pundibari West Bengal Cooch Behar736165 India ICAR NRC for Orchids Sikkim Pakyong737106 India ICAR Research Complex for NEH Region Sikkim Centre Tadong Sikkim Gangtok737102 India Department of Pomology and Post-harvest Technology Uttar Banga Krishi Viswavidyalaya Pundibari West Bengal Cooch Behar736165 India Soil Microbiology Laboratory Regional Research Station Uttar Banga Krishi Viswavidyalaya Pundibari Coochbehar736165 India Department of Agricultural Statistics & Computer Application Birsa Agricultural University Ranchi736165 India

The study was designed to elucidate the role of jackfruit clonal accessions with flake colour diversity from nutritional and medicinal perspective. Deep yellow coloured flake accession (UJCT 2, UJCT 3) contain highest flavonoids (152.77 mg QE /100g), DPPH activity (633.77 µg/100g), ascorbic acid (24.45 mg/100g), α-glucosidase inhibitory activity (276.98 µg/ml) whereas orange-red flake accession (UJCT 1) contain highest β-carotene (1.16 µg/100g), phenol (330.24 mg/100g), mineral (Fe and Zn) with better inhibition percentage of enzymes like α-amylase (2159.87 µg/ml), and β-glucosidase (22.61%). HPLC analysis revealed presence of higher sinapic acid (498.3 mg/kg), ferullic acid (407.7 mg/kg) and quercetin (4745.76 mg/kg) content in orange-red colour flake. GC-MS study revealed presence of anti-diabetic compounds (n-Hexadecanoic acid, tridecane, 2-Heptadecenal etc.) in deep yellow and orange-red coloured flakes with lower glycemic load ( © 2022, The Authors. All rights reserved.

关键词： Color

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Intrinsic mechanical behavior of MgAgSb thermoelectric material:An ab initio study

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Journal of Materiomics 2020年第1期6卷 24-32页

作者： Guodong Li Qi An Umut Aydemir Sergey I.Morozov Bo Duan Pengcheng Zhai Qingjie Zhang William A.Goddard Ⅲ Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics School of ScienceWuhan University of TechnologyWuhan430070China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan430070China Department of Chemical and Materials Engineering University of Nevada RenoRenoNV89557USA Department of Chemistry Koc UniversitySariyerIstanbul34450Turkey Boron and Advanced Materials Research Center Koc UniversitySariyerIstanbul34450Turkey Department of Computer Simulation and Nanotechnology South Ural State UniversityChelyabinsk454080Russia Materials and Process Simulation Center California Institute of TechnologyPasadenaCA91125USA

α-MgAgSb based thermoelectric(TE)device attracts much attention for its commercial application because it shows an extremely high conversion efficiency of ~8.5% under a temperature difference of 225 ***,the mechanical behavior of α-MgAgSb is another serious consideration for its engineering ***,we apply density functional theory(DFT)simulations to examine the intrinsic mechanical properties of all three MgAgSb phases,including elastic properties,shear-stress-shear-strain relationships,deformation and failure mechanism under ideal shear and biaxial shear *** find that the ideal shear strength of α-MgAgSb is 3.25 GPa along the most plausible(100)<010>slip *** strength is higher than that of β-MgAgSb(0.80 GPa)and lower than that of γ-MgAgSb(3.43 GPa).The failure of α-MgAgSb arises from the stretching and breakage of MgeSb bond α-MgAgSb under pure shear load,while it arises from the softening of MgeAg bond and the breakage of AgeSb bond under biaxial shear *** suggests that the deformation mechanism changes significantly under different loading conditions.

关键词： MgAgSb thermoelectric materials Density functional theory Ideal strength Deformation mechanism

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CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

引用

IEEE Transactions on Biometrics, Behavior, and Identity Science 2020年第2期2卷 182-193页

作者： Zhang, Shifeng Liu, Ajian Wan, Jun Liang, Yanyan Guo, Guodong Escalera, Sergio Escalante, Hugo Jair Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Faculty of Information Technology Macau University of Science and Technology 999078 China Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application Institute of Deep Learning Beijing100085 China Óptica y Electrónica Instituto Nacional de Astrofísica Puebla08007 Mexico Computer Science Department CINVESTAV-Zacatenco Mexico City07360 Mexico University of Chinese Academy of Sciences Beijing100049 China Westlake University Hangzhou310024 China

Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities (i.e., RGB, Depth and IR). We also provide comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://***/***/face-anti-spoofing/welcome/challengecvpr2019?authuser=0. © 2019 IEEE.

关键词： Face recognition

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ISPO: An Integrated Ontology of Symptom Phenotypes for Semantic Integration of Traditional Chinese Medical Data

arXiv

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arXiv 2024年

作者： Shu, Zixin Hua, Rui Yan, Dengying Lu, Chenxia Xu, Ning Li, Jun Zhu, Hui Zhang, Jia Zhao, Dan Hui, Chenyang Ye, Junqiu Liao, Chu Hao, Qi Ye, Wen Luo, Cheng Wang, Xinyan Cheng, Chuang Li, Xiaodong Liu, Baoyan Zhou, Xiaji Zhang, Runshun Xu, Min Zhou, Xuezhong Institute of Medical Intelligence School of Computer and Information Technology Beijing Jiaotong University Beijing100063 China Hubei Key Laboratory of the Theory and Application Research of Liver and Kidney in Traditional Chinese medicine Hubei Provincial Hospital of Traditional Chinese Medicine Wuhan430061 China National Data Center of Traditional Chinese Medicine China Academy of Chinese Medical Sciences Beijing100700 China Clinical College of Traditional Chinese Medicine Hubei University of Chinese Medicine Wuhan430000 China Department of Laboratory Medicine Hubei Provincial Hospital of Traditional Chinese Medicine Wuhan430061 China Department of Infectious Diseases Hubei Provincial Hospital of Traditional Chinese Medicine Wuhan430061 China Guang'anmen Hospital China Academy of Chinese Medical Sciences Beijing100053 China Information Technology Center the First Affiliated Hospital College of Medicine Zhejiang University Hangzhou310003 China

Background: Symptom phenotypes are one of the key types of manifestations for diagnosis and treatment of various disease conditions. However, the diversity of symptom terminologies is one of the major obstacles hindering the analysis and knowledge sharing of various types of symptom-related medical data particularly in the fields of Traditional Chinese Medicine (TCM). Objective: This study aimed to construct an Integrated Ontology of symptom phenotypes (ISPO) to support the data mining of Chinese EMRs and real-world study in TCM field. Methods: To construct an integrated ontology of symptom phenotypes (ISPO), we manually annotated classical TCM textbooks and large-scale Chinese electronic medical records (EMRs) to collect symptom terms with support from a medical text annotation system. Furthermore, to facilitate the semantic interoperability between different terminologies, we incorporated public available biomedical vocabularies by manual mapping between Chinese terms and English terms with cross-references to source vocabularies. In addition, we evaluated the ISPO using independent clinical EMRs to provide a high-usable medical ontology for clinical data analysis. Results: By integrating 78,696 inpatient cases of EMRs, 5 biomedical vocabularies, 21 TCM books and dictionaries, ISPO provides 3,147 concepts, 23,475 terms, and 55,552 definition or contextual texts. Adhering to the taxonomical structure of the related anatomical systems of symptom phenotypes, ISPO provides 12 top-level categories and 79 middle-level sub-categories. The validation of data analysis showed the ISPO has a coverage rate of 95.35%, 98.53% and 92.66% for symptom terms with occurrence rates of 0.5% in additional three independent curated clinical datasets, which can demonstrate the significant value of ISPO in mapping clinical terms to ontologies. Conclusions: ISPO delivers an integrated controlled vocabulary for symptom phenotypes that would enhance semantic interoperability among heterogene

关键词： Mapping

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Spatial fuzzy C-means clustering and deep belief network for change detection in synthetic aperture radar images 5

Spatial fuzzy C-means clustering and deep belief network for...

引用

IET International Radar Conference 2020, IET IRC 2020

作者： Qi, Wenwen Wu, Lin Guo, Zhengwei Huang, Dan College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475004 China College of Environment and Planning Henan University Kaifeng475004 China Department of Laboratory and Equipment Management Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

In this study, spatial fuzzy c-means (SFCM) clustering and deep belief network (DBN) method is presented for change detection in SAR images. There are three primary steps of this approach, they are given as follows: 1) Difference Image (DI) is firstly obtained by calculating the similarity of gray levels between multitemporal SAR images. 2) SFCM clustering is used to analyze DI, which combines the spatial information factor and the fuzzy cluster membership function, thus can effectively eliminate the interference of speckle noise. 3) DBN, a powerful data processing methodology that integrates deep learning techniques to extract underlying features and structures of complicated data, is trained to classify pixels to be changed or unchanged. The effectiveness and robustness of the proposed method are verified by experimental results with the Ottawa dataset. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Membership functions

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Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes

arXiv

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arXiv 2023年

作者： Huang, Shijie Zhang, Xukun Cui, Zhiming Zhang, He Chen, Geng Shen, Dinggang School of Biomedical Engineering ShanghaiTech University Shanghai China Academy for Eng. & Tech. Fudan University Shanghai China Department of Radiology Obstetrics and Gynecology Hospital of Fudan University Shanghai China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence company Ltd. Shanghai200030 China Shanghai Clinical Research Trial Center Shanghai201210 China

Accurate tissue segmentation of thick-slice fetal brain magnetic resonance (MR) scans is crucial for both reconstruction of isotropic brain MR volumes and the quantification of fetal brain development. However, this task is challenging due to the use of thick-slice scans in clinically-acquired fetal brain data. To address this issue, we propose to leverage high-quality isotropic fetal brain MR volumes (and also their corresponding annotations) as guidance for segmentation of thick-slice scans. Due to existence of significant domain gap between high-quality isotropic volume (i.e., source data) and thick-slice scans (i.e., target data), we employ a domain adaptation technique to achieve the associated knowledge transfer (from high-quality volumes to thick-slice scans). Specifically, we first register the available high-quality isotropic fetal brain MR volumes across different gestational weeks to construct longitudinally-complete source data. To capture domain-invariant information, we then perform Fourier decomposition to extract image content and style codes. Finally, we propose a novel Cycle-Consistent Domain Adaptation Network (C2DA-Net) to efficiently transfer the knowledge learned from high-quality isotropic volumes for accurate tissue segmentation of thick-slice scans. Our C2DA-Net can fully utilize a small set of annotated isotropic volumes to guide tissue segmentation on unannotated thick-slice scans. Extensive experiments on a large-scale dataset of 372 clinically acquired thick-slice MR scans demonstrate that our C2DA-Net achieves much better performance than cutting-edge methods quantitatively and qualitatively. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance

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GECAM Observations of the Galactic Magnetar SGR J1935+2154 during the 2021 and 2022 Burst Active Episodes. I. Burst Catalog

arXiv

引用

arXiv 2023年

作者： Xie, Sheng-Lun Cai, Ce Yu, Yun-Wei Xiong, Shao-Lin Lin, Lin Zhao, Yi Zhang, Shuang-Nan Song, Li-Ming Wang, Ping Li, Xiao-Bo Xue, Wang-Chen Zhang, Peng Zheng, Chao Zhang, Yan-Qiu Liu, Jia-Cong Wang, Chen-Wei Tan, Wen-Jun Wang, Yue Yu, Zheng-Hang Feng, Pei-Yi Zhang, Jin-Peng Xiao, Shuo Zhao, Hai-Sheng Zhang, Wen-Long Zhang, Yan-Ting Huang, Yue Zhao, Xiao-Yun Ma, Xiang Zheng, Shi-Jie Li, Xin-Qiao Wen, Xiang-Yang Gong, Ke An, Zheng-Hua Zhang, Da-Li Yang, Sheng Liu, Xiao-Jing Zhang, Fan Institute of Astrophysics Central China Normal University Wuhan430079 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences 19B Yuquan Road Beijing100049 China College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Hebei Shijiazhuang050024 China Department of Astronomy Beijing Normal University Beijing100088 China School of Computer and Information Dezhou University Dezhou253023 China University of Chinese Academy of Sciences Beijing100049 China College of Electronic and Information Engineering Tongji University Shanghai201804 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China School of Physics and Physical Engineering Qufu Normal University Shandong Qufu273165 China

Magnetar is a neutron star with an ultrahigh magnetic field (∼ 1014 − 1015 G). The magnetar SGR J1935+2154 is not only one of the most active magnetars detected so far, but also the unique confirmed source of fast radio bursts (FRBs). Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is dedicated to monitor gamma-ray transients all over the sky, including magnetar short bursts. Here we report the GECAM observations of the burst activity of SGR J1935+2154 from January 2021 to December 2022, which results in a unique and valuable data set for this important magnetar. With a targeted search of GECAM data, 159 bursts from SGR J1935+2154 are detected by GECAM-B while 97 bursts by GECAM-C, including the X-ray burst associated with a bright radio burst. We find that both the burst duration and the waiting time between two successive bursts follow lognormal distributions. The period of burst activity is 134 ± 20 days, thus the burst activity could be generally divided into four active episodes over these two years. Interestingly, the hardness ratio of X-ray bursts tends to be softer during these two years, especially during the active episode with radio bursts detected. © 2023, CC BY.

关键词： Galaxies

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