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arXiv 2021年

作者： Li, Zhaoqun Liang, Xu Fan, Dandan Li, Jinxing Jia, Wei Zhang, David The School of Data Science The Chinese University of Hong Kong Shenzhen The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China The Bio-Computing Research Center School of Computer Science and Technology Harbin Institute of Technology Shenzhen Shenzhen518055 China The School of Computer and Information Hefei University of Technology Hefei230009 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China

With the growing demand for hand hygiene and convenience of use, palmprint recognition with touchless manner made a great development recently, providing an effective solution for person identification. Despite many efforts that have been devoted to this area, it is still uncertain about the discriminative ability of the touchless palmprint, especially for large-scale datasets. To tackle the problem, in this paper, we build a large-scale touchless palmprint dataset containing 2334 palms from 1167 individuals. To our best knowledge, it is the largest touchless palmprint image benchmark ever collected with regard to the number of individuals and palms. Besides, we propose a novel deep learning framework for touchless palmprint recognition named 3DCPN (3D Convolution Palmprint recognition Network) which leverages 3D convolution to dynamically integrate multiple Gabor features. In 3DCPN, a novel variant of the Gabor filter is embedded into the first layer for enhancement of curve feature extraction. With a well-designed ensemble scheme, low-level 3D features are then convolved to extract high-level features. Finally on the top, we set a region-based loss function to strengthen the discriminative ability of both global and local descriptors. To demonstrate the superiority of our method, extensive experiments are conducted on our dataset and other popular databases TongJi and IITD, where the results show the proposed 3DCPN achieves state-of-the-art or comparable performances. Copyright © 2021, The Authors. All rights reserved.

关键词： Palmprint recognition

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Evaluating the Use of Large Language Model to Identify Top Research Priorities in Hematology

Evaluating the Use of Large Language Model to Identify Top R...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Huiqin Chen Fangwan Huang Xuanyun Liu Yinli Tan Dihua Zhang Dongqi Li Glen M. Borchert Ming Tan Jingshan Huang Department of Pediatrics The Third Affiliated Hospital Sun Yatsen University Guangzhou China College of Computer and Data Science Fuzhou University Fuzhou China Department of Nephrology The First Affiliated Hospital Sun Yat-sen University Guangzhou China Department of Economics & School of Information and Computer Sciences University of California Irvine Irvine U.S.A. Department of Pharmacology College of Medicine University of South Alabama Mobile U.S.A. Institute of Biochemistry and Molecular Biology Institute of Biomedical Sciences China Medical University Taichung Taiwan School of Computing College of Medicine University of South Alabama Mobile U.S.A.

The field of hematology is highly progressive and dynamic requiring researchers to commit significant amounts of time and effort towards staying abreast of the most crucial research areas. As such, in this work we assess the potential of ChatGPT for identifying research priorities within five key topics in hematology: acute lymphocytic leukemia, immunotherapy, targeted therapy, hematopoietic stem cell transplantation, and acute myeloid leukemia. After ChatGPT was employed to generate specific research questions in these areas, a panel of seven experienced hematologists independently reviewed and rated resultant research questions based on four parameters: relevance, originality, clarity, and specificity on a scale of 1 to 5, with 5 denoting the highest score. Excitingly, the mean and median grades of the four parameters were all above 4, indicating that the hematologists strongly agreed that the problems generated by ChatGPT were generally highly specific, clear, relevant, and original. As such, although further work will clearly be required, we suggest our current study indicates that Large Language Models (LLMs), such as ChatGPT, may very well represent valuable new tools for more efficiently identifying and prioritizing impactful research questions in the field of hematology.

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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Exploring the Spatial Correlation of Shadowing in RF-based Device-Free Localization by Block Sparse Bayesian Learning

Exploring the Spatial Correlation of Shadowing in RF-based D...

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IEEE International Conference on Robotics and Biomimetics

作者： Jiaju Tan Xin Zhao Xuemei Guo Guoli Wang School of Mathematics and Computational Science Wuyi University Jiangmen China Tianjin Key Laboratory of Intelligent Robotic (tjKLIR) Institute of Robotics and Automatic Information System (IRAIS) Nankai University Tianjin China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education School of Data and Computer Science Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781665405362

Device-Free Localization (DFL) realized by the Radio Frequency (RF) signal sensing, is an emerging technology to obtain the target’s position without needing the target carrying any electronic devices or tags. Due to its through-wall detectability and privacy protection, the RF-based DFL is becoming a promising research topic in the Location-based Services (LBS) applications based on Internet of Things (IoT). Among the RF-based DFL approaches, Radio Tomographic Imaging (RTI) is a low-cost and novel computational imaging method to reconstruct the target-induced shadowing effect in the RF monitored area and then estimate the target’s position. However, the multipath interference in the RF sensing network often induces the reconstruction degradation in the RTI system and leads to the position misestimation in DFL applications. Addressing this problem, considering that the target’s shadowing may occupy a small spatial range and then express some spatial structure, this aricle focuses on the spatial correlation of the target’s shadowing. Then an RTI reconstruction methods based on the Block Sparse Bayesian Learning is proposed to explore the spatial correlation in the sparse target’s shadowing. Moreover, the actual localization experiments are conducted to validate the spatial correlation in shadowing is an important property to improve the RTI reconstruction performance and DFL accuracy.

关键词： Performance evaluation Location awareness Radio frequency Correlation Robot sensing systems Bayes methods Sensors

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Beamforming Design for the Performance Optimization of Intelligent Reflecting Surface Assisted Multicast MIMO Networks

arXiv

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arXiv 2022年

作者： Zhang, Songling Yang, Zhaohui Chen, Mingzhe Liu, Danpu Wong, Kai-Kit Poor, H. Vincent The Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Hangzhou310007 China Zhejiang Lab. Hangzhou31121 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

In this paper, the problem of maximizing the sum of data rates of all users in an intelligent reflecting surface (IRS)-assisted millimeter wave multicast multiple-input multiple-output communication system is studied. In the considered model, one IRS is deployed to assist the communication from a multi-antenna base station (BS) to the multi-antenna users that are clustered into several groups. Our goal is to maximize the sum rate of all users by jointly optimizing the transmit beamforming matrices of the BS, the receive beamforming matrices of the users, and the phase shifts of the IRS. To solve this non-convex problem, we first use a block diagonalization method to represent the beamforming matrices of the BS and the users by the phase shifts of the IRS. Then, substituting the expressions of the beamforming matrices of the BS and the users, the original sum-rate maximization problem can be transformed into a problem that only needs to optimize the phase shifts of the IRS. To solve the transformed problem, a manifold method is used. Simulation results show that the proposed scheme can achieve up to 28.6% gain in terms of the sum rate of all users compared to the algorithm that optimizes the hybrid beamforming matrices of the BS and the users using our proposed scheme and randomly determines the phase shifts of the IRS. Copyright © 2022, The Authors. All rights reserved.

关键词： Multicasting

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Learning from pseudo lesion: A self-supervised framework for covid-19 diagnosis

arXiv

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arXiv 2021年

作者： Li, Zhongliang Jin, Zhihao Li, Xuechen Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019 and thoracic computed tomography (CT) has become one of the main tools for its diagnosis. In recent years, deep learning-based approaches have shown impressive performance in myriad image recognition tasks. However, they usually require a large number of annotated data for training. Inspired by Ground Glass Opacity (GGO), a common finding in COIVD-19 patient's CT scans, we proposed in this paper a novel self-supervised pretraining method based on pseudo lesions generation and restoration for COVID-19 diagnosis. We used Perlin noise, a gradient noise based mathematical model, to generate lesion-like patterns, which were then randomly pasted to the lung regions of normal CT images to generate pseudo COVID-19 images. The pairs of normal and pseudo COVID-19 images were then used to train an encoder-decoder architecture based U-Net for image restoration, which does not require any labelled data. The pretrained encoder was then fine-tuned using labelled data for COVID-19 diagnosis task. Two public COVID- 19 diagnosis datasets made up of CT images were employed for evaluation. Comprehensive experimental results demonstrated that the proposed self-supervised learning approach could extract better feature representation for COVID-19 diagnosis and the accuracy of the proposed method outperformed the supervised model pretrained on large scale images by 6.57% and 3.03% on SARS-CoV-2 dataset and Jinan COVID-19 dataset, respectively. © 2021, CC BY-NC-SA.

关键词： Coronavirus

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ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis

arXiv

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arXiv 2023年

作者： Zheng, Zhiling Zhang, Oufan Borgs, Christian Chayes, Jennifer T. Yaghi, Omar M. Department of Chemistry University of California BerkeleyCA94720 United States Kavli Energy Nanoscience Institute University of California BerkeleyCA94720 United States Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Department of Mathematics University of California BerkeleyCA94720 United States Department of Statistics University of California BerkeleyCA94720 United States School of Information University of California BerkeleyCA94720 United States KACST-UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications King Abdulaziz City for Science and Technology Riyadh11442 Saudi Arabia

We use prompt engineering to guide ChatGPT in the automation of text mining of metal-organic frameworks (MOFs) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT's tendency to hallucinate information-an issue that previously made the use of Large Language Models (LLMs) in scientific fields challenging. Our approach involves the development of a workflow implementing three different processes for text mining, programmed by ChatGPT itself. All of them enable parsing, searching, filtering, classification, summarization, and data unification with different tradeoffs between labor, speed, and accuracy. We deploy this system to extract 26,257 distinct synthesis parameters pertaining to approximately 800 MOFs sourced from peer-reviewed research articles. This process incorporates our ChemPrompt Engineering strategy to instruct ChatGPT in text mining, resulting in impressive precision, recall, and F1 scores of 90-99%. Furthermore, with the dataset built by text mining, we constructed a machine-learning model with over 87% accuracy in predicting MOF experimental crystallization outcomes and preliminarily identifying important factors in MOF crystallization. We also developed a reliable data-grounded MOF chatbot to answer questions on chemical reactions and synthesis procedures. Given that the process of using ChatGPT reliably mines and tabulates diverse MOF synthesis information in a unified format, while using only narrative language requiring no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be very useful across various other chemistry subdisciplines. Copyright © 2023, The Authors. All rights reserved.

关键词： data mining

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Group-wise inhibition based feature regularization for robust classification

arXiv

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arXiv 2021年

作者： Liu, Haozhe Wu, Haoqian Xie, Weicheng Liu, Feng Shen, Linlin 1Computer Vision Institute College of Computer Science and Software Engineering 2SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society 3National Engineering Laboratory for Big Data System Computing Technology 4Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen 518060 China

The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e.g. corrupted and adversarial samples). One of the possible reasons is that CNN pays more attention to the most discriminative regions, but ignores the auxiliary features when learning, leading to the lack of feature diversity for final judgment. In our method, we propose to dynamically suppress significant activation values of CNN by group-wise inhibition, but not fixedly or randomly handle them when training. The feature maps with different activation distribution are then processed separately to take the feature independence into account. CNN is finally guided to learn richer discriminative features hierarchically for robust classification according to the proposed regularization. Our method is comprehensively evaluated under multiple settings, including classification against corruptions, adversarial attacks and low data regime. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both robustness and generalization performances, when compared with the state-of-the-art methods. Code is available at https://***/LinusWu/TENET_Training. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review

arXiv

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arXiv 2023年

作者： Cheng, Sibo Quilodrán-Casas, César Ouala, Said Farchi, Alban Liu, Che Tandeo, Pierre Fablet, Ronan Lucor, Didier Iooss, Bertrand Brajard, Julien Xiao, Dunhui Janjic, Tijana Ding, Weiping Guo, Yike Carrassi, Alberto Bocquet, Marc Arcucci, Rossella Data Science Institute Department of Computing Imperial College London LondonSW7 2AZ United Kingdom Department of Earth Science and Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Computer Science and Engineering Hong Kong University of Science and Technology 999077 Hong Kong IMT Atlantique Lab-STICC UMR CNRS 6285 France and Odyssey Inria/IMT France RIKEN Center for Computational Science Kobe Japan CEREA École des Ponts and EDF R&D île-de-France France The Laboratoire Interdisciplinaire des Sciences du Numérique CNRS Paris-Saclay University OrsayF-91403 France 78401 Chatou France Institut de Mathématiques de Toulouse Toulouse31062 France SINCLAIR AI Lab Saclay France Bergen Norway School of Mathematical Sciences Tongji University Shanghai200092 China Mathematical Institute for Machine Learning and Data Science KU Eichstätt-Ingolstadt Bavaria Germany School of Information Science and Technology Nantong University Nantong226019 China Department of Physics and Astronomy Augusto Righi University of Bologna Bologna40124 Italy

data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFD) to geoscience and climate systems. Recently, much effort has been given in combining DA, UQ and machine learning (ML) techniques. These research efforts seek to address some critical challenges in high-dimensional dynamical systems, including but not limited to dynamical system identification, reduced order surrogate modelling, error covariance specification and model error correction. A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains, resulting in the necessity for a comprehensive guide. This paper provides the first overview of the state-of-the-art researches in this interdisciplinary field, covering a wide range of applications. This review aims at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models, but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems. Therefore, this article has a special focus on how ML methods can overcome the existing limits of DA and UQ, and vice versa. Some exciting perspectives of this rapidly developing research field are also discussed. © 2023, CC BY-NC-SA.

关键词： Dynamical systems

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COVID-19 in Malaysia and the trusted elements of mobile health records management issues

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AIP Conference Proceedings 2023年第1期2827卷

作者： Fatin Nur Zulkipli Azman Ismail Nurussobah Hussain Mohd Fakhrul Fahmi Sarwani Saiful Farik Mat Yatin Information Science Studies College of Computing Informatics and Media Universiti Teknologi MARA Puncak Perdana Selangor Malaysia Information Science Studies College of Computing Informatics and Media Universiti Teknologi MARA (UiTM) Kelantan Branch Kelantan Malaysia Universiti Kuala Lumpur Malaysian Institute of Marine Engineering Technology Lumut Perak Malaysia Meridian Project Management Sdn Bhd Cyberjaya Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Selangor Malaysia

The novel Coronavirus (COVID-19) global pandemic has become a challenge for the healthcare industry and the digitalization era. Thus, it is high time for government hospitals to implement one of the Eleventh Malaysia Planning (11MP) strategies to integrate the transformation of the mobile application systems with future-ready interfaces. The reliability of a healthcare records system that includes vital information about patients like demographics and medical history requires high-security measures. The COVID-19 outbreak highlights the importance of the trustworthiness of the healthcare system through a stronger partnership between security and information technology. However, the lack of standards concerning the safety and storage of records and legal compliance are crucial issues for mobile health records management (MHRM). Since the outbreak, the Malaysian government has launched the “MySejahtera” mobile platform to educate citizens on effective information dissemination. However, the increased COVID-19 testing highlights the shortfall of “MySejahtera,” suggesting that enhancements to the existing system are much needed. Thus, this study aims to analyze mobile health records’ trustworthiness in Malaysian hospitals by creating a reliable framework. A qualitative approach using descriptive and comparative data will be employed. The study’s outcome is expected to be a guide for Malaysian healthcare in providing conducive environments for all.

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