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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yun-Chi Chen Tung Chao Wimaya Nitya Phandita Tzu-Yu Sun Huai-Zhi Wang Yi-Hsien Hsieh Le-Yin Hsu Che Lin Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University (NTU) Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan Data Science Degree Program NTU Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

关键词： Attention mechanisms Histopathology Computational modeling Precision medicine Small satellites Immunotherapy Colorectal cancer Predictive models Transformers Genetics

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Deep STI: Deep Stochastic Time-series Imputation on Electronic Health Records

Deep STI: Deep Stochastic Time-series Imputation on Electron...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ming-Che Cheng Yi-Hsien Hsieh Te-Cheng Hsu Tung-Hung Su Che Lin Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Dept. of Internal Medicine Division of Gastroenterolgy and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Electronic Health Records (EHRs) are a cornerstone of modern healthcare analytics, offering rich datasets for various disease analyses through advanced deep learning algorithms. However, the pervasive issue of missing values in EHRs significantly hampers the development and performance of these models. Addressing this challenge is crucial for enhancing clinical decision-making and patient care. Existing methods for handling missing data, ranging from simple imputation to more sophisticated approaches, often fall short of capturing the temporal dynamics inherent in EHRs. To bridge this gap, we introduce the Deep Stochastic Time-series Imputation (Deep STI) algorithm, an innovative end-to-end deep learning model that seamlessly integrates a sequence-to-sequence generative network with a prediction network. Deep STI is designed to leverage the observed time-series data in EHRs, learning to infer missing values from the temporal context with high accuracy. We evaluated Deep STI on the liver cancer data from the National Taiwan University Hospital (NTUH), Taiwan. Our results showed that Deep STI achieved better 5-year hepatocellular carcinoma predictions (19.21% in the area under the precision-recall curve) than extreme gradient boosting (18.15%) and Transformer (18.09%). The ablation study also illustrates the efficacy of our generative architecture design compared to regular imputations. This approach not only promises to improve the reliability of disease analysis in the presence of incomplete data but also sets a new standard for utilizing EHRs in predictive healthcare. Our work aims to advance the field of healthcare analytics and open new avenues for research in deep learning applications to EHRs.

关键词： Deep learning Time series analysis Stochastic processes Predictive models Transformers Imputation Reliability Electronic medical records Standards Diseases

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Telemedicine with digital video transport system in Asia -Pacific area

Telemedicine with digital video transport system in Asia -Pa...

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19th International Conference on Advanced Information Networking and Applications, AINA 2005

作者： Nakashima, Naoki Okamura, Koji Hahm, Joon-Soo Kim, Young-Woo Mizushima, Hiroshi Tatsumi, Haruyuki Moon, Byung-In Han, Ho-Seong Park, Yong-Jin Lee, Jae-Hwa Youm, Sung-Kwan Kang, Chul-Hee Shimizu, Shuji Department of Medical Informatics Kyushu University Faculty of Medicine Fukuoka Japan Computing and Communications Center Kyushu University Fukuoka Japan Department of Medicine Hanyang University Medical College Seoul Korea Republic of Research Institute and Hospital National Cancer Center Goyang Gyeonggi Korea Republic of Research Institute National Cancer Center Tokyo Japan Signal Transduction and Medicine Sapporo Medical University Graduate School of Medicine Sapporo Japan Department of Surgery Breast Center Ewha WomansMokdong Hospital Seoul Korea Republic of Department of Surgery Seoul National University Bundang Hospital Seoul Korea Republic of Division of Electrical and Computer Engineering Hanyang University Seoul Korea Republic of Korea Advanced Research Network Seoul Korea Republic of School of Electrical Engineering Korea University Seoul Korea Republic of Department of Endoscopic Diagnostics and Therapeutics Kyushu University Faculty of Medicine Fukuoku Japan

ISBN: (纸本)0769522491

We have established a medical network in Asia-Pacific area with high-quality moving image on a super broad-banded internet line since February 2003. Broadcasting real-time surgery and teleconference with medical-quality videos with Digital Video Transport System (DVTS) were so useful to learn surgical techniques and other medical procedures beyond borders. We have conducted 20 events in categories of teleconference (8 times), live transmission of medical procedure (6 times), and remote attendance to scientific medical meetings (6 times) until September 2004. We had teleconferences among 3 countries in APAN-Hawaii in January 2004 (Japan, Korea and USA), and in APAN-Cairns in July 2004 (Japan, Korea and Australia). There have been no serious transmission/ ethical problems sofar. With these experiences and current achievements, we hope to extend this advanced network system to whole Asia-Pacific countries to share medical skills and knowledge. © 2005 IEEE.

关键词： Telemedicine

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Shun-Cheng Chang Hsin-Pei Yu Yi-Hsien Hsieh Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan Institute of Applied Mathematical Sciences NTU Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag.

关键词： Visualization Accuracy Three-dimensional displays Liver Transformers Planning Convolutional neural networks Context modeling Biomedical imaging Tumors

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Shun-Cheng Chang Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan NTU Institute of Applied Mathematical Sciences Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan NTU Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages.

关键词： Liver cancer Accuracy Computed tomography Biological system modeling Transformers Planning Biomedical imaging

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Dynamic programming Algorithm using Furniture Industry 0/1 Knapsack Problem

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Mietha Anditta Natashya Amartya Laurens Spits Warnars Harco Leslie Hendric Spits Warnars Arief Ramadhan Teddy Siswanto Teddy Mantoro Nurulhuda Noordin Computer Science Departement School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Departement School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Departement BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia PJJ Informatika Telkom University Bandung Indonesia Information Systems Department Trisakti University Jakarta Indonesia Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Malaysia

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset.

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Preliminary Design for Reference tracking feedback for Medical Cyber-Physical Pharmacokinetic Systems

Preliminary Design for Reference tracking feedback for Medic...

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IEEE International Conference on Automatica (ICA-ACCA)

作者： Pedro M. Oliveira Daniel Bustos Rodolfo Lobo Márcio J. Lacerda Jonathan M. Palma Graduate Program in Electrical Engineering Federal University of Minas Gerais Belo Horizonte Brazil Dept. of Translational Medicine Universidad Católica del Maule Talca Chile Ingenieria en Telecomunicaciones e Informática Universidad Diego Portales Santiago Chile Centre for Communications Technology School of Computing and Digital Media London Metropolitan University London UK Faculty of Engineering Universidad de Talca Curicó Chile

ISBN: (数字)9798350378115

ISBN: (纸本)9798350378122

This paper addresses the reference tracking control problem for Medical Cyber-Physical Systems (MCPS). The control theory is employed to guarantee the suitable concentration of drugs in the body of patients to guarantee a safe treatment. The MCPS is modeled as a switched system, and the modes consider the different scenarios for the problem. A discrete-time model is utilized for the pharmacokinetic process, and the zero input control strategy is employed to design state-feedback controllers with a guaranteed exponential convergence rate. A numerical experiment is presented to illustrate the validity and effectiveness of our method.

关键词： Drugs Switched systems Design methodology Process control Medical services Cyber-physical systems Numerical models Control theory Pharmacokinetics Convergence

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Is Attention All You Need for EEG to Predict Neurological Outcomes in Cardiac Arrest?

Is Attention All You Need for EEG to Predict Neurological Ou...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Jefferson Dionisio Muhammad Rizwan Hidetaka Suzuki Kazi Mahmudul Hassan Ingon Chanpornpakdi Lian-Yu Lin Toshihisa Tanaka Che Lin Department of Smart Medicine and Health Informatics National Taiwan University Taipei Taiwan Artificial Intelligence of Things Taiwan International Graduate Program Academia Sinica Taipei Taiwan Tokyo University of Agriculture and Technology Tokyo Japan Department of Emergency and Critical Care Japanese Red Cross Musashino Hospital Tokyo Japan Department of Internal Medicine Division of Cardiology National Taiwan University College of Medicine and Hospital Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine National Taiwan University Taipei Taiwan

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Sudden cardiac arrest (SCA) poses a significant health challenge, necessitating accurate predictions of neurological outcomes in comatose patients, where good outcomes are defined as the recovery of most cognitive functions. Electroencephalogram (EEG) serves as a valuable biomarker for monitoring neurological states due to its rich, time-dependent information. This study aims to predict neurological outcomes using early EEG data by employing the Transformer model, which leverages multi-headed attention to identify patterns in lengthy sequences such as hour-long EEG recordings. Unlike traditional methods that use subsampled EEG epochs, we utilize the entire EEG sequences, subdivided into time steps, allowing our model to capture detailed temporal patterns via the attention mechanism. Moreover, we trained our proposed model using each EEG recording as an individual data sample but evaluated our model through aggregated patient-wise predictions. This approach allows us to boost the data sample size. Our results demonstrate promising predictive performance, achieving an AUROC of 0.82 and AUPRC of 0.90 on the holdout test set and an AUROC of 0.73 and AUPRC of 0.93 on an external test set with patient-wise predictions. This study highlights the potential of utilizing attention mechanisms to capture important time series progressions across EEG sequences for improving SCA prognosis.

关键词： Attention mechanisms Biological system modeling Time series analysis Cardiac arrest Predictive models Brain modeling Transformers Electroencephalography Data models Recording

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Appointments for 2005-2006 term

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IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2005年第7期13卷 773-782页

作者： Ranganathan, Nagarajan Agrawal, Vishwani D. Chakradhar, Srimat T. Chakrabarty, Krishnendu Courtois, Bernard DeMara, Ronald Hu, Xiaobo Sharon Ismail, Yehea I. Jha, Niraj K. John, Lizy Kurian Ker, Ming-Dou Koren, Israel Liu, Bin-Da Marculescu, Diana Marculescu, Radu Narayanan, Vijaykrishnan Nassif, Sani R. Nowick, Steven M. Sapatnekar, Sachin S. Sherlekar, Sunil Sylvester, Dennis Vemuri, Ranga Pham, Michael Department of Computer Science and Engineering University of South Florida Tampa FL 33620 United States Department of Computer Science and Engineering Nanomaterials and Nanomanufacturing Research Center University of South Florida Tampa United States IEEE United States Systems United States Department of Electrical and Computer Engineering Rutgers University New Brunswick NJ United States IETE India ACM NEC Laboratories America Inc. Princeton NJ United States Duke University Durham NC United States ACM SIGDA Sigma Xi United States Laboratory of Techniques of Informatics and Microelectronics for Computer Architectecture ASME IMAPS IEEE Computer Society University of Central Florida Orlando United States Computer Engineering Program SEECS Department of Computer Science and Engineering University of Notre Dame Notre Dame IN United States Northwestern University Evanston IL United States Department of Electrical Engineering Princeton University Princeton NJ United States Association for Computing Machinery Austin United States UT Austin Engineering Foundation United States Department of Electronics Engineering National Chiao-Tung University Taiwan Department of Electrical and Computer Engineering University of Massachusetts Amherst United States Department of Electrical Engineering Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States Electrical and Computer Engineering Faculty Carnegie Mellon University Pittsburgh PA United States Computer Science and Engineering Department Pennsylvania State University University Park United States IBM Austin Research Laboratory Austin TX United States Columbia University New York NY United States Department of Electrical and Computer Engineering University of Minnesota Minneapolis United States Tata Consultancy Services Centre of Excellence for Embedded Systems India Department of Electrical Engineering IIT Bombay India Department of Electrical Engineering Uni

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Reports of the AAAI 2011 conference workshops

Reports of the AAAI 2011 conference workshops

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作者： Agmon, Noa Agrawal, Vikas Aha, David W. Aloimonos, Yiannis Buckley, Donagh Doshi, Prashant Geib, Christopher Grasso, Floriana Green, Nancy Johnston, Benjamin Kaliski, Burt Kiekintveld, Christopher Law, Edith Lieberman, Henry Mengshoel, Ole J. Metzler, Ted Modayil, Joseph Oard, Douglas W. Onder, Nilufer O'Sullivan, Barry Pastra, Katerina Precup, Doina Ramachandran, Sowmya Reed, Chris Sariel-Talay, Sanem Selker, Ted Shastri, Lokendra Singh, Satinder Smith, Stephen F. Srivastava, Siddharth Sukthankar, Gita Uthus, David C. Williams, Mary-Anne Department of Computer Science University of Texas at Austin United States Center for Knowledge Driven Information Systems Infosys Labs India Adaptive Systems Section Naval Research Laboratory Washington DC United States Department of Computer Science University of Maryland College Park United States EMC's Center of Excellence in Ireland Research Europe at EMC Ireland Department of Computer Science University of Georgia United States School of Informatics University of Edinburgh United Kingdom Department of Computer Science University of Liverpool United Kingdom Department of Computer Science University of North Carolina Greensboro United States Faculty of Engineering and Information Technology University of Technology Sydney Australia VeriSign Inc. United States Department of Computer Science University of Texas at El Paso United States Machine Learning Department Carnegie Mellon University United States Massachusetts Institute of Technology Media Laboratory United States Carnegie Mellon University's Silicon Valley Campus United States Darrell W. Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computing Science University of Alberta Canada College of Information Studies Institute for Advanced Computer Studies University of Maryland College Park United States Department of Computer Science Michigan Technological University United States Department of Computer Science University College Cork Ireland Cognitive Systems Research Insitute Athens Greece School of Computer Science McGill University Canada Stottler Henke Inc. United States School of Computing University of Dundee United Kingdom Department of Computer Engineering Istanbul Technical University Turkey Convergence Lab Infosys Technologies Ltd. United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Robotics Institute Carnegie Mellon University United States University of Wisconsin

The AAAI-11 workshop program was held Sunday and Monday, August 7-18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages;Analyzing Microtext;Applied Adversarial Reasoning and Risk Modeling;Artificial Intelligence and Smarter Living: The Conquest of Complexity;Artifiicial Intelligence for Data Center Management and Cloud computing;Automated Action Planning for Autonomous Mobile Robots;Computational Models of Natural Argument;Generalized Planning;Human Computation;Human-Robot Interaction in Elder Care;Interactive Decision Theory and Game Theory, 2010;Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action, and Language;Lifelong Learning from Sensorimotor Experience;Plan, Activity, and Intent Recognition;and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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