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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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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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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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Optimizing Low-Energy Carbon IIoT Systems with Quantum Algorithms: Performance Evaluation and Noise Robustness

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IEEE Internet of Things Journal 2025年

作者： Dave, Kshitij Innan, Nouhaila Behera, Bikash K. Mumtaz, Shahid Al-Kuwari, Saif Farouk, Ahmed The Institute of Advanced Research Gandhinagar India Quantum Physics and Magnetism Team LPMC Faculty of Sciences Ben M’sick Hassan II University of Casablanca Morocco United Arab Emirates NYUAD Research Institute NYUAD Abu Dhabi United Arab Emirates Pvt. Ltd. WB Mohanpur741246 India Nottingham Trent University Engineering Department United Kingdom The Qatar Center for Quantum Computing College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar The Department of Computer Science Faculty of Computers and Artificial Intelligence Hurghada University Hurghada Egypt

Low-energy carbon Internet of Things (IoT) systems are essential for sustainable development, as they reduce carbon emissions while ensuring efficient device performance. Although classical algorithms manage energy efficiency and data processing within these systems, they often face scalability and real-time processing limitations. Quantum algorithms offer a solution to these challenges by delivering faster computations and improved optimization, thereby enhancing both the performance and sustainability of low-energy carbon IoT systems. Therefore, we introduced three quantum algorithms: quantum neural networks utilizing Pennylane (QNN-P), Qiskit (QNN-Q), and hybrid quantum neural networks (QNN-H). These algorithms are applied to two low-energy carbon IoT datasets—room occupancy detection (RODD) and GPS tracker (GPSD). For the RODD dataset, QNN-P achieved the highest accuracy at 0.95, followed by QNN-H at 0.91 and QNN-Q at 0.80. Similarly, for the GPSD dataset, QNN-P attained an accuracy of 0.94, QNN-H 0.87, and QNN-Q 0.74. Furthermore, the robustness of these models is verified against six noise models. The proposed quantum algorithms demonstrate superior computational efficiency and scalability in noisy environments, making them highly suitable for future low-energy carbon IoT systems. These advancements pave the way for more sustainable and efficient IoT infrastructures, significantly minimizing energy consumption while maintaining optimal device performance. © 2014 IEEE.

关键词： Zero-carbon

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Development of a Nursing Skill Training System Based on Manipulator Variable Admittance Control

Development of a Nursing Skill Training System Based on Mani...

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IEEE/ASME (AIM) International Conference on advanced Intelligent Mechatronics

作者： Yuhao Zhou Ryota Takamido Masako Kanai-Pak Jukai Maeda Yasuko Kitajima Mitsuhiro Nakamura Noriaki Kuwahara Taiki Ogata Jun Ota Department of Precision Engineering Graduate School of Engineering The University of Tokyo Tokyo Japan Research Into Artifacts Center for Engineering The University of Tokyo Tokyo Japan Faculty of Nursing Kanto Gakuin University Yokohama Japan Faculty of Nursing Tokyo Ariake University of Medical and Health Sciences Tokyo Japan Department of Advanced Fibro-Science Kyoto Institute of Technology Kyoto Japan Department of Computer Science School of Computing Tokyo Institute of Technology Tokyo Japan

The use of robot-based skill training systems is an emerging topic in nursing education, as many innovative robotic systems have been developed to simulate real patients, offering a safe and self-directed platform for nursing students to learn and practice their skills. Among these training systems, several human patient simulators (HPS) have been proposed to simulate the patients performance during patient transfer; however, without an entire motion model and control strategy, most HPS show limited effectiveness in simulating actual patient behavior. Herein, this work presents a novel patient transfer training system that has the potential of improving the practical skills of nursing students. First, we propose a simplified force model for patient transfer motion to estimate the contact force in the absence of wearable sensors. We then reveal the correlation between the nurses force and patients motion during the transfer through the utilization of the variable admittance model. Finally, we demonstrate the feasibility of the proposed patient transfer training system by performing several experiments on a UR10e robot. To the best of our knowledge, this system is the first patient transfer skills training system that simulates force interaction between nurse and patient using a collaborative robot.

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A novel machine learning hybrid model for heart disease risk assessment

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AIP Conference Proceedings 2025年第1期3224卷

作者： Saurabh Srivastava Tasneem Ahmed Amit Saxena Rajeev Kumar Department of Computer Science & Engineering Moradabad Institute of Technology Moradabad India Advanced Computing and Research Laboratory Dept. of Computer Application Integral University Lucknow India

Heart disease claims millions of lives annually in more than a hundred countries. Heart disease has become one of the world’s leading causes of mortality, according to a World Health Organization (WHO) report. Among the conditions that can cause heart attacks include angina pectoris, congestive heart failure, cardiomyopathy, congenital heart disease, arrhythmias, myocarditis, heart attacks, and heart cancer. Heart disease presents a significant global health challenge, prompting extensive research into early detection and preventive measures. This study introduces a predictive model leveraging advanced machine learning (ML) techniques to assess heart disease risk. The model development process encompasses several phases, including data preprocessing, feature selection, and model training employing state-of-the-art algorithms like logistic regression (LR), decision tree (DT), random forests (RF), artificial neural network (ANN), naïve Bayes (NB), K-Nearest Neighbor (KNN), and support vector machine (SVM). Additionally, feature importance analysis and model evaluation techniques are utilized to refine interpretability and performance. The results exhibit the model’s efficacy in accurately predicting heart disease risk, offering valuable insights for early detection and personalized healthcare strategies. Overall, this research emphasizes the potential of data-driven methodologies in constructing predictive models for heart disease, presenting a promising avenue for enhancing patient outcomes and alleviating the burden on global healthcare systems.

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A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications

arXiv

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arXiv 2022年

作者： Xia, Jun Zhu, Yanqiao Du, Yuanqi Li, Stan Z. School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Computer Science George Mason University United States

Pre-trained Language Models (PLMs) such as BERT have revolutionized the landscape of natural language processing (NLP). Inspired by their proliferation, tremendous efforts have been devoted to pre-trained graph models (PGMs) recently. Owing to its huge model parameters, PGMs can capture abundant knowledge from massive labeled and unlabeled graph data. The knowledge implicitly encoded in model parameters can benefit various downstream tasks and help to alleviate several fundamental issues of learning on graphs. In this paper, we provide a comprehensive survey for PGMs. We first briefly present the limitations of graph representation learning and thus introduce the motivation for graph pre-training. Next, we systematically categorize existing PGMs based on a taxonomy from five different perspectives including the history, model architectures, pre-training strategies, tunning strategies and applications. Finally, we outline several promising research directions that can serve as a guideline for future studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Taxonomies

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Cloud-Powered Neural Networks for Sustainable Fishing Practices and Stock Management System

Cloud-Powered Neural Networks for Sustainable Fishing Practi...

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Sustainable Expert Systems (ICSES), International Conference on

作者： M. Xavier Suresh Prathik A V. Arun R. Venugopal T. R. GaneshBabu M. Muthulekshmi Department of Physics School of Advanced Sciences and Languages VIT Bhopal University Bhopal Madhya Pradesh India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Mathematics Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (DU) Salem Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Sustainable fishing methods and efficient stock management are crucial considering growing environmental concerns; fishing practices and effective stock management are imperative. This research presents a novel approach that uses neural networks (NN) hosted in the cloud to transform sustainable fishing and stock management. This method trains NN to make predictions by combining several data sources, such as satellite images, weather patterns, and catch records. These models can reliably predict fish stocks' dynamics, giving stakeholders the information needed to make informed decisions. Crucial for real-time processing and storage of massive information is the scalability and flexibility offered by cloud infrastructure. This framework provides the appropriate usage of resources while simultaneously optimizing fishing tactics, which helps protect marine ecosystems. It demonstrates that method promotes sustainable fishing via thorough case studies and simulations. It provides a flexible and scalable solution to the problems confronting marine biodiversity and the fishing industry by combining advanced technology with ecological principles. To ensure a more egalitarian and sustainable future for fisheries management on a global scale, method is a giant leap forward in balancing commercial interests with environmental protection. Integrating data, ensuring sensors are accurate, managing huge datasets, managing regulatory compliance, and encouraging stakeholders to work together for sustainable practices are all challenges.

关键词： Cloud computing Technological innovation Biological system modeling Decision making Artificial neural networks Predictive models Fisheries Real-time systems Stakeholders Resource management

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Hamiltonian Simulation with Random Inputs

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Physical Review Letters 2022年第27期129卷 270502-270502页

作者： Qi Zhao You Zhou Alexander F. Shaw Tongyang Li Andrew M. Childs Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland 20742 USA QICI Quantum Information and Computation Initiative Department of Computer Science The University of Hong Kong Pokfulam Road Hong Kong Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education) Fudan University Shanghai 200433 China Nanyang Quantum Hub School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Center on Frontiers of Computing Studies Peking University Beijing 100871 China School of Computer Science Peking University Beijing 100871 China Center for Theoretical Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Computer Science and Institute for Advanced Computer Studies University of Maryland College Park Maryland 20742 USA

The algorithmic error of digital quantum simulations is usually explored in terms of the spectral norm distance between the actual and ideal evolution operators. In practice, this worst-case error analysis may be unnecessarily pessimistic. To address this, we develop a theory of average-case performance of Hamiltonian simulation with random initial states. We relate the average-case error to the Frobenius norm of the multiplicative error and give upper bounds for the product formula (PF) and truncated Taylor series methods. As applications, we estimate average-case error for the digital Hamiltonian simulation of general lattice Hamiltonians and k-local Hamiltonians. In particular, for the nearest-neighbor Heisenberg chain with n spins, the error is quadratically reduced from O(n) in the worst case to O(n) on average for both the PF method and the Taylor series method. Numerical evidence suggests that this theory accurately characterizes the average error for concrete models. We also apply our results to error analysis in the simulation of quantum scrambling.

关键词： Quantum algorithms Quantum computation Quantum simulation

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A transfer learning based model for brain tumor detection using magnetic resonance imaging

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AIP Conference Proceedings 2025年第1期3224卷

作者： Saurabh Srivastava Tasneem Ahmed Rajeev Kumar Amit Saxena Department of Computer Science & Engineering Moradabad Institute of Technology Moradabad India Advanced Computing and Research Laboratory Dept. of Computer Application Integral University Lucknow India

Rapid and uncontrolled proliferation of cells is the cause of brain tumors. If treatment is not carried out early, death can occur. A major challenge in detecting a brain tumor is that it originates from variations in size, shape, and location. Since tumor cells are diverse, it might be difficult for radiologists to classify brain tumors, which are malignant diseases. The overall survival of patients, treatment planning, and risk factor identification depend on robust and accurate tumor classification and prediction. In the medical field, deep learning (DL) and machine learning (ML) are often employed to diagnose illnesses. Various tools have been developed that are used to diagnose different diseases such as heart disease, lung cancer, and many more. In this study, a DL-based transfer learning technique has been used to build a model that can easily identify whether a patient is suffering from a brain tumor or not. Brain tumors have been categorized into four types of categories based on the characteristics of the tumor. Patients who are suffering from the first and second categories of tumors can be saved by treatments that include surgery, radiation therapy, and chemotherapy. The third and fourth categories of tumors are more dangerous because these categories of tumors spread very fast, so if it is diagnosed early stage then the patient can be saved or increase the patient’s life span. Glioma, Meningioma, and Pituitary are a dangerous form of tumors, and the survival rate of patients who are suffering from these is very low. The developed system can predict that a patient is suffering from a brain tumor or not using MRI, and also predict the type of tumor such as Glioma, Meningioma, and Pituitary.

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