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arXiv 2019年

作者： Zabihi, Morteza Rad, Ali Bahrami Kiranyaz, Serkan Särkkä, Simo Gabbouj, Moncef Department of Computing Sciences Tampere University Tampere Finland Department of Electrical Engineering and Automation Aalto University Espoo Finland Electrical Engineering Department Qatar University Doha Qatar

Sleep arousals transition the depth of sleep to a more superficial stage. The occurrence of such events is often considered as a protective mechanism to alert the body of harmful stimuli. Thus, accurate sleep arousal detection can lead to an enhanced understanding of the underlying causes and influencing the assessment of sleep quality. Previous studies and guidelines have suggested that sleep arousals are linked mainly to abrupt frequency shifts in EEG signals, but the proposed rules are shown to be insufficient for a comprehensive characterization of arousals. This study investigates the application of five recent convolutional neural networks (CNNs) for sleep arousal detection and performs comparative evaluations to determine the best model for this task. The investigated state-of-the-art CNN models have originally been designed for image or speech processing. A detailed set of evaluations is performed on the benchmark dataset provided by PhysioNet/computing in Cardiology Challenge 2018, and the results show that the best 1D CNN model has achieved an average of 0.31 and 0.84 for the area under the precision-recall and area under the ROC curves, respectively. Copyright © 2019, The Authors. All rights reserved.

关键词： Sleep research

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Potential of multimodal large language models for data mining of medical images and free-text reports

Meta-Radiology

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Meta-Radiology 2024年第4期2卷

作者： Zhang, Yutong Pan, Yi Zhong, Tianyang Dong, Peixin Xie, Kangni Liu, Yuxiao Jiang, Hanqi Wu, Zihao Liu, Zhengliang Zhao, Wei Zhang, Wei Zhao, Shijie Zhang, Tuo Jiang, Xi Shen, Dinggang Liu, Tianming Zhang, Xin Institute of Medical Research Northwestern Polytechnical University Xi'an 710072 China School of Computing The University of Georgia Athens 30602 United States School of Life Science and Technology University of Electronic Science and Technology of China Chengdu 611731 China School of Automation Northwestern Polytechnical University Xi'an 710072 China School of Electronics and Information Northwestern Polytechnical University Xi'an 710072 China School of Biomedical Engineering ShanghaiTech University Shanghai 201210 China Department of Radiology The Second Xiangya Hospital Central South University Changsha 410011 China Clinical Research Center for Medical Imaging in Hunan Province Changsha 410011 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer and Cyber Sciences Augusta University Augusta 30912 United States Shanghai United Imaging Intelligence Co. Ltd. Shanghai 200230 China Shanghai Clinical Research and Trial Center Shanghai 201210 China

Medical images and radiology reports are essential for physicians to diagnose medical conditions. However, the vast diversity and cross-source heterogeneity inherent in these data have posed significant challenges to the generalizability of current data-mining methods for clinical decision-making. Recently, multimodal large language models (MLLMs), especially Gemini-Vision-series (Gemini) and GPT-4-series (GPT-4) models, have revolutionized numerous domains, significantly impacting the medical field. In this study, we conducted a detailed evaluation of the performance of the Gemini series models (including Gemini-1.0-Pro-Vision, Gemini-1.5-Pro, and Gemini-1.5-Flash) and GPT series models (including GPT-4o, GPT-4-Turbo, and GPT-3.5-Turbo) across 14 medical datasets, covering 5 medical imaging categories (dermatology, radiology, dentistry, ophthalmology, and endoscopy) and 3 radiology report datasets. The investigated tasks encompass disease classification, lesion segmentation, anatomical localization, disease diagnosis, report generation, and lesion detection. Moreover, we also validated the performance of the Claude-3-Opus, Yi-Large, Yi-Large-Turbo, and LLaMA 3 models to gain a comprehensive understanding of the MLLM models in the medical field. Our experimental results demonstrated that Gemini-series models excelled in report generation and lesion detection but faces challenges in disease classification and anatomical localization. Conversely, GPT-series models exhibited proficiency in lesion segmentation and anatomical localization but encountered difficulties in disease diagnosis and lesion detection. Additionally, both the Gemini series and GPT series contain models that have demonstrated commendable generation efficiency. While both models hold promise in reducing physician workload, alleviating pressure on limited healthcare resources, and fostering collaboration between clinical practitioners and artificial intelligence technologies, substantial enhancements

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Robust multi-objective control for vehicle active suspension systems with structured uncertainties

Robust multi-objective control for vehicle active suspension...

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Chinese Control Conference (CCC)

作者： Zhong Cao Sheng-Guo Wang Zhaohui Chen Xiaorong Hou Wenjing Zhao School of Physics and Electronic Engineering Guangzhou University Guangzhou China College of Engineering and College of Computing Informatics University of North Carolina at Charlotte Charlotte NC USA Department of Mathematics and Physics Chongqing University of Science and Technology Chongqing China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Lab Center Guangzhou University Guangzhou China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

A robust structural control method is proposed to solve the problems of structured uncertainties and multi-objective control for vehicle active suspension systems. The considered model is a quarter model of vehicle subject to unknown ground disturbance and structured uncertainties. The proposed method is based on robust structured control well supported by singular value decomposition (SVD) method, yielding a multi-objective control design approach, applicable to almost any active suspension systems. Some experiments show that the control performance of robust structural control for vehicle active suspension systems with structured uncertainties is significantly improved comparing with passive controlled system and existing other controlled active suspension systems.

关键词： Suspensions Uncertainty Robustness Roads Mathematical model Robust control Control systems

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Focalizing Regions of Biomarker-Relevance Facilitates Biomarker Prediction on Histopathological Images

SSRN

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SSRN 2023年

作者： Gan, Jiefeng Wang, Hanchen Yu, Hui He, Zitong Zhang, Wenjuan Ma, Ke Zhu, Lianghui Bai, Yutong Zhou, Zongwei Yullie, Alan Bai, Xiang Wang, Mingwei Yang, Dehua Chen, Yanyan Chen, Guoan Lasenby, Joan Cheng, Chao Wu, Jia Zhang, Jianjun Wang, Xinggang Chen, Yaobing Wang, Guoping Xia, Tian School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Engineering University of Cambridge Cambridge United Kingdom Computing + Mathematical Sciences Department California Institute of Technology PasadenaCA United States Wuhan Children's Hospital Tongji Medical College China Department of Computer Science Johns Hopkins University United States Department of Pathology Maternal and Child Hospital of Hubei Province Tongji Medical College Huazhong University of Science and Technology Wuhan China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Department of Radiology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China The National Center for Drug Screening Shanghai Institute of Materia Medica Chinese Academy of Sciences Shanghai China Department of Information Management Tongji Hospital Huazhong University of Science and Technology Wuhan China Wuhan Blood Center Wuhan China Baylor College of Medicine HoustonTX United States Department of Radiation Oncology Stanford University School of Medicine Palo AltoCA United States Thoracic/Head and Neck Medical Oncology Translational Molecular Pathology The University of Texas MD Anderson Cancer Center HoustonTX United States Institute of Pathology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China

Image-based AI has thrived as a potential revolutionised tool for predicting the status of molecular biomarkers, which effectively helps group patients with targeted medical treatments. However, methods based on haematoxylin and eosin-stained (H&E) whole-slide images (WSIs), usually accommodate the entire images directly as model inputs, which could be inefficient as many image patches are not informative or even irrelevant to the biomarkers. In this study, we introduced a new concept of region of biomarker-relevance (ROB) to represent the morphological regions most related to a specific biomarker and to serve as a fundamental element for WSI-based biomarker prediction. We embodied the ROB theory with framework titled saliency ROB search (SRS) to effectively predict biomarker status. We exemplified the SRS by analyzing various adenocarcinoma (LUAD) biomarkers, and achieved better performances than the-state-art of AI approaches. This indicates that on the basis of ROB, AI can reach a better molecular biomarker prediction precision from pathological images. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision

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Integrate and Fire Neuron Implementation using CMOS Predictive Technology Model for 32nm

Integrate and Fire Neuron Implementation using CMOS Predicti...

引用

Microelectronics Technology and Devices (SBMicro)

作者： Gabriel Maranhão Janaina G. Guimarães Department of Electrical Engineering Universidade de Brasília Brasília Brazil Department of Control Automation and Computing Engineering Universidade Federal de Santa Catarina Blumenau Brazil

The propose of this work is to evolve the studies on neuromorphic circuits by simulating transistors models that look the most with the ones used in commercial process. Since most of data about company models contain a restricted access, some universities provide predictions models to reproduce the real ones. In this paper we present a silicon analog integrate and fire neuron (I&F), proposed by G. Indiveri as a part of a neuromorphic device. Using 32nm CMOS technology simulated in LTspice with BSIM4v4 and applying predictive parameters provides by Predictive Technology Model (PTM), we were able to reduce the source power, to 0.9V, and chip size of the latest design which was implemented using a 180nm CMOS process at 1.8 power supply.

关键词： Neurons Predictive models Logic gates Semiconductor device modeling Transistors Mathematical model Computational modeling

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Unknown input observer-based robust sensor fault estimation in discrete-time Takagi-Sugeno systems 44

Unknown input observer-based robust sensor fault estimation ...

引用

44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018

作者： Chaves, Emanoel R.Q. Maitelli, André L. Lopes, Kennedy R. Andrade, Ana C.C. De Lima, Bernardo F.A. Department of Computing Engineering and Automation Federal University of Rio Grande do Norte Natal RN Brazil Laboratory of Automation in Petroleum Natal RN Brazil

ISBN: (纸本)9781509066841

In this paper, a robust fault estimation technique is proposed to estimate the states and sensor faults simultaneously in a discrete-time Takagi-Sugeno (TS) fuzzy system with uncertainties. Initially, an augmented system is obtained from fault dynamics model and from the original system. Then an robust TS fuzzy unknown input observer (UIO) is designed for the augmented system with the aim of performing decoupling of the process disturbances. The UIO design is achieved by an H∞ technique, which is developed to maintain the UIO stable and to reduce the disturbances that cannot be decoupled. The proposed method is validated through a simulation example. © 2018 IEEE.

关键词： Uncertainty analysis

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TDFI: Two-stage Deep Learning Framework for Friendship Inference via Multi-source Information

TDFI: Two-stage Deep Learning Framework for Friendship Infer...

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2019 IEEE Conference on Computer Communications, INFOCOM 2019

作者： Zhao, Yi Qiao, Meina Wang, Haiyang Zhang, Rui Wang, Dan Xu, Ke Tan, Qi Department of Computer Science and Technology Tsinghua University China China School of Automation Science and Electrical Engineering Beihang University China Department of Computer Science University of Minnesota at Duluth United States School of Computer Science and Center for OPTIMA Northwestern Polytechnical University China Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong

ISBN: (纸本)9781728105154

Due to the explosive growth of social network services, friendship inference has been widely adopted by Online Social Service Providers (OSSPs) for friend recommendation. The conventional techniques, however, have limitations in accuracy or scalability to handle such a large yet sparse multi-source data. For example, the OSSPs will be required to manually give the order in which the various information is applied. This unavoidably reduces the applicability of existing friend recommendation systems. To address this issue, we propose a Two-stage Deep learning framework for Friendship Inference (TDFI). This approach can utilize multi-source information simultaneously with low complexity. In particular, we apply an Extended Adjacency Matrix (EAM) to represent the multi-source information. We then adopt an improved Deep AutoEncoder Network (iDAEN) to extract the fused feature vector for each user. The TDFI framework also provides an improved Deep Siamese Network (iDSN) to measure user similarity from iDAEN. Finally, we evaluate the effectiveness and robustness of TDFI on three large-scale real-world datasets. It shows that TDFI can effectively handle the sparse multi-source data while providing better accuracy for friend recommendation. © 2019 IEEE.

关键词： Large dataset

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Data and Physics Driven Learning Models for Fast MRI - Fundamentals and Methodologies from CNN, GAN to Attention and Transformers

arXiv

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arXiv 2022年

作者： Huang, Jiahao Fang, Yingying Nan, Yang Wu, Huanjun Wu, Yinzhe Gao, Zhifan Li, Yang Wang, Zidong Lio, Pietro Rueckert, Daniel Eldar, Yonina C. Yang, Guang The National Heart and Lung Institute Imperial College London Guy Scadding Building Dovehouse St LondonSW3 6LY United Kingdom The Department of Bioengineering Imperial College London South Kensington LondonSW7 2AZ United Kingdom The School of Biomedical Engineering Sun Yat-Sen University Guangdong Guangzhou510006 China The School of Automation Sciences and Electrical Engineering Beihang University Beijing China The Department of Computer Science Brunel University London LondonUB8 3PH United Kingdom The Computer Laboratory University of Cambridge CambridgeCB3 0FD United Kingdom The Department of Computing Imperial College London South Kensington Campus LondonSW7 2AZ United Kingdom Technical University of Munich Munich80333 Germany Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

Research studies have shown no qualms about using data driven deep learning models for downstream tasks in medical image analysis, e.g., anatomy segmentation and lesion detection, disease diagnosis and prognosis, and treatment planning. However, deep learning models are not the sovereign remedy for medical image analysis when the upstream imaging is not being conducted properly (with artefacts). This has been manifested in MRI studies, where the scanning is typically slow, prone to motion artefacts, with a relatively low signal to noise ratio, and poor spatial and/or temporal resolution. Recent studies have witnessed substantial growth in the development of deep learning techniques for propelling fast MRI. This article aims to (1) introduce the deep learning based data driven techniques for fast MRI including convolutional neural network and generative adversarial network based methods, (2) survey the attention and transformer based models for speeding up MRI reconstruction, and (3) detail the research in coupling physics and data driven models for MRI acceleration. Finally, we will demonstrate through a few clinical applications, explain the importance of data harmonisation and explainable models for such fast MRI techniques in multicentre and multi-scanner studies, and discuss common pitfalls in current research and recommendations for future research directions. © 2022, CC BY-NC-ND.

关键词： Signal to noise ratio

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Evaluation of OpenAI o1: Opportunities and Challenges of AGI

arXiv

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arXiv 2024年

作者： Zhong, Tianyang Liu, Zhengliang Pan, Yi Zhang, Yutong Zhou, Yifan Liang, Shizhe Wu, Zihao Lyu, Yanjun Shu, Peng Yu, Xiaowei Cao, Chao Jiang, Hanqi Chen, Hanxu Li, Yiwei Chen, Junhao Hu, Huawen Liu, Yihen Zhao, Huaqin Xu, Shaochen Dai, Haixing Zhao, Lin Zhang, Ruidong Zhao, Wei Yang, Zhenyuan Chen, Jingyuan Wang, Peilong Ruan, Wei Wang, Hui Zhao, Huan Zhang, Jing Ren, Yiming Qin, Shihuan Chen, Tong Li, Jiaxi Zidan, Arif Hassan Jahin, Afrar Chen, Minheng Xia, Sichen Holmes, Jason Zhuang, Yan Wang, Jiaqi Xu, Bochen Xia, Weiran Yu, Jichao Tang, Kaibo Yang, Yaxuan Sun, Bolun Yang, Tao Lu, Guoyu Wang, Xianqiao Chai, Lilong Li, He Lu, Jin Sun, Lichao Zhang, Xin Ge, Bao Hu, Xintao Zhang, Lian Zhou, Hua Zhang, Lu Zhang, Shu Liu, Ninghao Jiang, Bei Kong, Linglong Xiang, Zhen Ren, Yudan Liu, Jun Jiang, Xi Bao, Yu Zhang, Wei Li, Xiang Li, Gang Liu, Wei Shen, Dinggang Sikora, Andrea Zhai, Xiaoming Zhu, Dajiang Zhang, Tuo Liu, Tianming Department of Mathematical and Statistical Sciences University of Alberta Edmonton Canada School of Computing University of Georgia GA United States Institute of Medical Research Northwestern Polytechnical University Xi'An China College of Arts and Sciences University of Georgia Athens United States Institute of Plant Breeding Genetics & Genomics University of Georgia AthensGA United States Department of Computer Science and Engineering University of Texas ArlingtonTX United States The Lamar Dodd School of Art University of Georgia GA United States School of Computer Science Northwestern Polytechnical University Xi'An China School of Automation Northwestern Polytechnical University Xi'An China University of California Los AngelesCA United States Department of Radiology The Second Xiangya Hospital Central South University Changsha China Clinical Research Center for Medical Imaging in Hunan Province Changsha China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Guanghua School of Management Peking University Beijing China Department of Radiation Oncology Mayo Clinic PhoenixAZ United States Second Language Acquisition and Teaching University of Arizona TucsonAZ United States School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China The First Hospital of Hebei Medical University Hebei Shijiazhuang China School of Computer and Cyber Sciences Augusta University AugustaGA United States School of Physics and Information Technology Shaanxi Normal University Xi'An China School of Future Technology South China University of Technology Guangzhou China Department of Radiology and BRIC University of North Carolina Chapel HillNC United States Department of Educational Psychology University of Georgia AthensGA United States Johns Hopkins University BaltimoreMD United States School of Architecture Tsinghua Universit

This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguistics, and social sciences. Through rigorous testing, o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance in areas ranging from coding challenges to scientific reasoning and from language processing to creative problem-solving. Key findings include: • 83.3% success rate in solving complex competitive programming problems, surpassing many human experts. • Superior ability in generating coherent and accurate radiology reports, outperforming other evaluated models. • 100% accuracy in high school-level mathematical reasoning tasks, providing detailed step-by-step solutions. • Advanced natural language inference capabilities across general and specialized domains like medicine. • Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis. • Remarkable proficiency in anthropology and geology, demonstrating deep understanding and reasoning in these specialized fields. • Strong capabilities in quantitative investing. O1 has comprehensive financial knowledge and statistical modeling skills. • Effective performance in social media analysis, including sentiment analysis and emotion recognition. The model excelled particularly in tasks requiring intricate reasoning and knowledge integration across various fields. While some limitations were observed, including occasional errors on simpler problems and challenges with certain highly specialized concepts, the overall results indicate significant progress towards artificial general intelligence. This evaluation not only highlights o1-preview's current strengths and limitations but also identifies crucial areas for future development, including multi-modal integratio

关键词： Emotion Recognition

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