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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Peng, Tao Zhao, Jing Gu, Yidong Di, Gongye Zhang, Lei Cai, Jing School of Future Science and Engineering Soochow University Suzhou China Ut Southwestern Medical Center Department of Radiation Oncology DallasTX United States Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital Department of Ultrasound Beijing China The Affiliated Suzhou Hospital of Nanjing Medical University Suzhou Municipal Hospital Department of Medical Ultrasound Jiangsu Suzhou China The Affiliated Taizhou People's Hospital of Nanjing Medical University Department of Ultrasonic Taizhou China Duke Kunshan University Graduate Program of Medical Physics Data Science Research Center Jiangsu Kunshan China The Hong Kong Polytechnic University Department of Health Technology and Informatics Hong Kong

ISBN: (纸本)9798350337020

Ultrasound image segmentation is crucial for early disease detection and treatment planning but remains a challenging task due to the low contrast of organ boundaries and varying image quality. Current methods often require manual intervention or have limited accuracy. In this paper, we propose a novel hybrid framework that combines an automatic option polygon segment (AOPS) algorithm and a distributed- and memory-based evolution (DME) algorithm for precise ultrasound organ segmentation. Our pipeline consists of two cascaded stages: (1) a coarse segmentation step using the AOPS algorithm, which determines the number of vertices/clusters without human intervention, and (2) a refinement step using the DME algorithm to hunt for the optimal neural network, which is then used to represent a smooth, explainable mathematical expression of the organ boundary. We employ the fractional backpropagation learning network with L2 regularization (FBLN) for training and use the scaled exponential linear unit (SELU) activation function to address the vanishing gradient problem. This is a new attempt such a hybrid framework is applied to ultrasound organ segmentation tasks, and it demonstrates significant contributions in terms of accuracy, smoothness, and computational efficiency. © 2023 IEEE.

关键词： distributed-evolution learning network Multi-organs segmentation principal curve SEL U-based explainable mathematical model ultrasound

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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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Development of Educational Game for Indonesian History Lesson Focusing on Indonesia Pre-Literacy Relics 'Prehistoria' for Grade X High School Student

Development of Educational Game for Indonesian History Lesso...

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2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020

作者： Nugroho, Supeno Mardi Susiki Rahman, Muhammad Daffa Abiyyu Hariadi, Mochamad Arief, Muhammad Faculty of Intelligent Electrical and Informatics Technology Sepuluh Nopember Institute of Technology Computer Engineering Department Surabaya Indonesia Tokyo University of Technology Media Science Program Graduate School of Bionics Computer and Media Sciences Tokyo Japan

ISBN: (纸本)9781728182834

The development of game technology has opened up new possibilities in learning methods through educational games. However, in Indonesia, the application of this concept is lacking. One lesson that can benefit from educational games is Indonesian History in class X, with a focus on history in the pre-literacy period. This study aims to create a puzzle-Adventure type of digital game that can help educate students about the pre-literacy period, developed with Unity, and uses WebGL as a target device while referring to Indonesia's revised 2013 national curriculum. The result of this research is the creation of a digital game that can be played on average of 254.36 seconds, with potential for educational use. A survey result shows that many of the ancient objects and some of the ages are well-recognized. However, the survey also shows many of the gameplay aspects, such as UI, gameplay features, and adventure elements, need improvements. © 2020 IEEE.

关键词： Surveys

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Understanding ADC variation by fat content effect using a dual-function MRI phantom

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European Radiology Experimental 2024年第1期8卷 19页

作者： Liu, Yi-Jui Tsai, Tung-Sheng Li, Ya-Hui Peng, Jo-Hua Chang, Hing-Chiu Peng, Hsu-Hsia Lee, Ying-Chieh Lee, Tung-Yang Liou, Chang-Hsien Lin, Tz-Feng Chew, Fatt-Yang Chou, Ruey-Hwang Juan, Chun-Jung Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital 199 Sec. 1 Xinglong Rd. Hsinchu County Zhubei City 302 Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Biomedical Engineering Chinese University of Hong Kong Hong Kong Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Institute of Nuclear Engineering and Science National Tsing Hua University Hsinchu Taiwan Department of Fiber and Composite Materials Feng Chia University Taichung Taiwan Department of Medical Imaging China Medical University Hospital Taichung Taiwan Department of Radiology School of Medicine China Medical University Taichung Taiwan Graduate Institute of Biomedical Sciences China Medical University Taichung 406 Taiwan Center for Molecular Medicine China Medical University Hospital Taichung Taiwan Department of Medical Laboratory and Biotechnology Asia University Taichung Taiwan

Background: A dual-function phantom designed to quantify the apparent diffusion coefficient (ADC) in different fat contents (FCs) and glass bead densities (GBDs) to simulate the human tissues has not been documented yet. We propose a dual-function phantom to quantify the FC and to measure the ADC at different FCs and different GBDs. Methods: A fat-containing diffusion phantom comprised by 30 glass-bead-containing fat-water emulsions consisting of six different FCs (0, 10, 20, 30, 40, and 50%) multiplied by five different GBDs (0, 0.1, 0.25, 0.5, and 1.0 g/50 mL). The FC and ADC were measured by the “iterative decomposition of water and fat with echo asymmetry and least squares estimation-IQ,” IDEAL-IQ, and single-shot echo-planar diffusion-weighted imaging, SS-EP-DWI, sequences, respectively. Linear regression analysis was used to evaluate the relationship among the fat fraction (FF) measured by IDEAL-IQ, GBD, and ADC. Results: The ADC was significantly, negatively, and linearly associated with the FF (the linear slope ranged from -0.005 to -0.017, R² = 0.925 to 0.986, all p < 0.001). The slope of the linear relationship between the ADC and the FF, however, varied among different GBDs (the higher the GBD, the lower the slope). ADCs among emulsions across different GBDs and FFs were overlapped. Emulsions with low GBDs plus high FFs shared a same lower ADC range with those with median or high GBDs plus median or lower FFs. Conclusions: A novel dual-function phantom simulating the human tissues allowed to quantify the influence of FC and GBD on ADC. Relevance statement: The study developed an innovative dual-function MRI phantom to explore the impact of FC on ADC variation that can affect clinical results. The results revealed the superimposed effect on FF and GBD density on ADC measurements. Key points: • A dual-function phantom made of glass bead density (GBD) and fat fraction (FF) emulsion has been developed. • Apparent diffusion coefficient (ADC) values

关键词： Adipose tissue Diffusion magnetic resonance imaging Phantoms (imaging) Quality assurance (healthcare) Quality control

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Gut microbial signatures associated with uremic pruritus in hemodialysis patients

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Journal of Microbiology, Immunology and Infection 2025年第4期58卷 479-485页

作者： Ko, Mei-Ju Liao, Chun-Hsing Chiu, Yen-Ling Tsai, Wan-Chuan Yang, Ju-Yeh Pai, Mei-Fen Tsai, Ping-Hsiu Hsu, Shih-Ping Peng, Yu-Sen Wu, Hon-Yen Department of Dermatology Taipei City Hospital Taipei City Taiwan Department of Dermatology National Taiwan University Hospital and College of Medicine Taipei City Taiwan School of Medicine College of Medicine National Yang Ming Chiao Tung University Taipei City Taiwan University of Taipei Taipei City Taiwan Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Graduate Institute of Medicine Yuan Ze University Taoyuan City Taiwan Center for General Education Lee-Ming Institute of Technology New Taipei City Taiwan School of Life Science National Taiwan Normal University Taipei City Taiwan Department of Applied Cosmetology Lee-Ming Institute of Technology New Taipei City Taiwan Department of Healthcare Administration Asia Eastern University of Science and Technology New Taipei City Taiwan Department of Internal Medicine National Taiwan University Hospital and College of Medicine Taipei City Taiwan Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei City Taiwan

Background: The gut microbiota influences the gut–skin–kidney axis, but its role in uremic pruritus remains poorly understood. This study aimed to explore differences in gut microbial profiles between hemodialysis (HD) patients with and without uremic pruritus and identify potential microbial signatures associated with uremic pruritus. Methods: We conducted a cross-sectional study of HD patients with and without uremic pruritus. Stool samples were collected from all participants, and the gut microbiota composition was analyzed using 16S rRNA gene sequencing. Alpha and beta diversity metrics were calculated to assess microbial diversity. LEfSe analysis was performed to identify differentially abundant taxa associated with uremic pruritus. Results: Among 93 HD patients (mean age: 61.9 years, 31.2 % female), uremic pruritus occurred in 61.3 % of patients, with a median visual analog scale (VAS) score of 4.0. While alpha diversity did not differ significantly between groups, beta diversity analysis revealed significant compositional differences (unweighted UniFrac metric, P = 0.004;weighted UniFrac metric, P < 0.001). LEfSe analysis revealed significant enrichment of the order Pasteurellales, family Pasteurellaceae and genus Dialister in patients with uremic pruritus, whereas the order Corynebacteriales was more abundant in patients without pruritus (P < 0.05, LDA score > 3). Conclusion: In this study, we found significant differences in gut microbiota composition between HD patients with and without uremic pruritus and identified potential microbial biomarkers for uremic pruritus. Further studies are needed to elucidate the underlying mechanisms and explore microbiota-targeted therapeutic interventions. © 2025

关键词： Dialysis Gastrointestinal microbiome Microbiota Pruritus Skin diseases Uremia

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Model-Based Analysis of Microservice Resiliency Patterns

Model-Based Analysis of Microservice Resiliency Patterns

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Working IEEE/IFIP Conference on Software Architecture

作者： Nabor C. Mendonca Carlos M. Aderaldo Javier Camara David Garlan Post Graduate Program in Applied Informatics University of Fortaleza Brazil Post Graduate Program in Applied Informatics University of Fortaleza CE Brazil University of York York UK School of Computer Science Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9781728146591

ISBN: (纸本)9781728146607

Microservice application developers try to mitigate the impact of partial outages typically by implementing service-to-service interactions that use well-known resiliency patterns, such as Retry, Fail Fast, and Circuit Breaker. However, those resiliency patterns-as well as their available open-source implementations-are often documented informally, leaving it up to application developers to figure out when and how to use those patterns in the context of a particular microservice application. In this paper, we take a first step towards improving on this situation by introducing a model checking-based approach in which we use the PRISM probabilistic model checker to analyze the behavior of the Retry and Circuit Breaker resiliency patterns as continuous-time Markov chains (CTMC). This approach has enabled us to quantify the impact of applying each resiliency pattern on multiple quality attributes, as well as to determine how to best tune their parameters to deal with varying service availability conditions, in the context of a simple client-service interaction scenario.

关键词： Circuit breakers Resilience Integrated circuit modeling Probabilistic logic Transient analysis Circuit faults Analytical models

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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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COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2

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Briefings in Bioinformatics 2023年第5期24卷 bbad280-bbad280页

作者： Lim, Hendrick Gao-Min Fann, Yang C Lee, Yuan-Chii Gladys Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei 11031 Taiwan Department of Medical Research Tzu Chi Hospital Indonesia Pantai Indah Kapuk Greater Jakarta 14470 Indonesia It and Bioinformatics Program Division of Intramural National Institute of Neurological Disorders and Stroke National Institutes of Health Bethesda 20892 MD United States

Implementing a specific cloud resource to analyze extensive genomic data on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a challenge when resources are limited. To overcome this, we repurposed a cloud platform initially designed for use in research on cancer genomics (https://***) to enable its use in research on SARS-CoV-2 to build Cloud Workflow for Viral and Variant Identification (COWID). COWID is a workflow based on the Common Workflow Language that realizes the full potential of sequencing technology for use in reliable SARS-CoV-2 identification and leverages cloud computing to achieve efficient parallelization. COWID outperformed other contemporary methods for identification by offering scalable identification and reliable variant findings with no false-positive results. COWID typically processed each sample of raw sequencing data within 5 min at a cost of only US$0.01. The COWID source code is publicly available (https://***/hendrick0403/COWID) and can be accessed on any computer with Internet access. COWID is designed to be user-friendly;it can be implemented without prior programming knowledge. Therefore, COWID is a time-efficient tool that can be used during a pandemic. © 2023 Published by Oxford University Press.

关键词： cloud repurposing cloud workflow COVID-19 Illumina sequencing parallel computation SARS-CoV-2

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Mini Light-Emitting Diode Technology with High Quantum Efficient NIR-II Partially Inverse Spinel MgGa2O4:Cr3+,Ni2+ Nanophosphors

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Advanced Optical Materials 2024年第19期12卷

作者： Satpathy, Aishwarya Huang, Wen-Tse Liu, Tzu-Hsuan Su, Ting-Yi Zhang, Wei Kamiński, Mikolaj Grzegorczyk, Maciej Chen, Jia-Hao Cherng, Ding-Hua Lu, Kuang-Mao Chen, Xueyuan Mahlik, Sebastian Liu, Ru-Shi Department of Chemistry National Taiwan University Taipei106 Taiwan Fujian Key Laboratory of Nanomaterials and CAS Key Laboratory of Design and Assembly of Functional Nanostructures Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fujian Fuzhou350002 China Nanoscience and Technology Program Taiwan International Graduate Program Academia Sinica and National Taiwan University Academia Road 128 Taipei Nankang115 Taiwan Institute of Experimental Physics Faculty of Mathematics Physics and Informatics University of Gdańsk Wita Stwosza 57 Gdańsk80–308 Poland Everlight Electronics Co. Ltd. New Taipei City238 Taiwan International Centre for Theory of Quantum Technologies University of Gdańsk Gdańsk80–308 Poland

The increasing demand for second near-infrared (NIR-II) region materials, which retain the advantage of minimal scattering and immense applications in the medical and NIR spectroscopy field, has led to considerable research in this region. A mini light-emitting diode (mini-LED) is essential for backlighting liquid crystal displays, indicating the need for the small size of phosphors. However, current market phosphors must be more significant to be a viable option for mini-LEDs. This scenario necessitates the synthesis of small-sized phosphors to be used in mini-LEDs. In this work, a mesoporous silica nanoparticle (MSN) and incorporated the Mg1−yGa2−xO4:xCr3+,yNi2+ system is fabricated. The structural investigation confirmed the single phase of MgGa2O4 with a pore size of 2.73 nm for the MSN. The results showed a steady NIR-II signal at 1270 nm and an enhanced energy transfer with a high quantum yield of 79.2% for a nanophosphor. The mini-LED package revealed a 1000–1600 nm signal, qualifying the nanophosphor for realistic applications. This work can make provisions for various NIR-II nanophosphors in the LED industry. © 2024 Wiley-VCH GmbH.

关键词： Energy transfer

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