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Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN

IEEE/ACM Transactions on Computational Biology and Bioinformatics 2023年第5期20卷 3267-3277页

作者： Tu, Deng-Yao Lin, Peng-Chan Chou, Hsin-Hung Shen, Meng-Ru Hsieh, Sun-Yuan National Cheng Kung University Master Degree Program on Artificial Intelligence Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Informatics Department of Oncology Department of Genomic Medicine National Cheng Kung University Hospital College of Medicine Department of Computer Science and Information Engineering Tainan City70101 Taiwan National Chi Nan University Department of Computer Science and Information Engineering Nantou County54561 Taiwan National Cheng Kung University Graduate Institute of Clinical Medicine Department of Obstetrics and Gynecology Department of Pharmacology National Cheng Kung University Hospital College of Medicine Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Information Institute of Manufacturing Information and Systems Center for Innovative FinTech Business Models International Center for the Scientific Development of Shrimp Aquaculture Department of Computer Science and Information Engineering Tainan City70101 Taiwan

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models. © 2004-2012 IEEE.

关键词： Tumors

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ECG signals-based security and steganography approaches in WBANs: A comprehensive survey and taxonomy

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Sustainable Computing: informatics and Systems 2024年 41卷

作者： Masdari, Mohammad Band, Shahab S. Qasem, Sultan Noman Sayed, Biju Theruvil Pai, Hao-Ting Department of Computer Engineering Urmia Branch Islamic Azad University Urmia Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Riyadh11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz6803 Yemen Department of Computer Science Dhofar University PO Box 2509 SalalahPCode 211 Oman Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan

Wireless Body Area Networks (WBANs) are integral components of e-healthcare systems, responsible for monitoring patients' physiological states through intelligent implantable or wearable sensor nodes. These nodes collect medical data, which is then transmitted to remote healthcare facilities for thorough evaluation. Securing medical data within WBANs is paramount due to its central role in preserving patient privacy and confidentiality. Notably, Electrocardiogram (ECG) signals have recently gained prominence as pivotal elements within diverse security frameworks. Incorporating ECG signals strategically enhances the security and reliability of WBANs and broader e-healthcare systems, instilling greater trustworthiness. This survey article provides an in-depth exploration of contemporary ECG-based security schemes, adding to the scholarly discourse. The imperative to categorize these security paradigms revolves around their use of ECG signals. This categorization identifies three key domains: the first involves schemes that utilize ECG signals for cryptographic operations, encompassing key generation, agreement, management, and authentication. The second category employs steganography-based techniques, using ECG signals to conceal patients' sensitive medical data. The third category focuses on enhancing ECG signal security during data transmission. Each category is meticulously elaborated, detailing architectural foundations, notable contributions, and intrinsic security services. Furthermore, each section presents a comprehensive overview of the attributes characterizing ECG-based security frameworks. This includes insights into employed datasets, simulation environments, evaluation metrics, and inherent advantages and limitations. Expanding on this, a thorough analysis of distinctive attributes underpinning these security frameworks concludes by shedding light on potential directions for future research. © 2023 Elsevier Inc.

关键词： Authentication

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Revised JNLPBA corpus: A revised version of biomedical NER corpus for relation extraction task

arXiv

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arXiv 2019年

作者： Huang, Ming-Siang Lai, Po-Ting Tsai, Richard Tzong-Han Hsu, Wen-Lian Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang Ming University Taipei Taiwan Department of Computer Science National Tsing-Hua University Hsinchu Taiwan Intelligent Agent Systems Laboratory Institute of Information Science Academia Sinica Taipei Taiwan Intelligent Information Service Research Laboratory Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan

The advancement of biomedical named entity recognition (BNER) and biomedical relation extraction (BRE) researches promotes the development of text mining in biological domains. As a cornerstone of BRE, robust BNER system is required to identify the mentioned NEs in plain texts for further relation extraction stage. However, the current BNER corpora, which play important roles in these tasks, paid less attention to achieve the criteria for BRE task. In this study, we present Revised JNLPBA corpus, the revision of JNLPBA corpus, to broaden the applicability of a NER corpus from BNER to BRE task. We preserve the original entity types including protein, DNA, RNA, cell line and cell type while all the abstracts in JNLPBA corpus are manually curated by domain experts again basis on the new annotation guideline focusing on the specific NEs instead of general terms. Simultaneously, several imperfection issues in JNLPBA are pointed out and made up in the new corpus. To compare the adaptability of different NER systems in Revised JNLPBA and JNLPBA corpora, the F1-measure was measured in three open sources NER systems including BANNER, Gimli and NERSuite. In the same circumstance, all the systems perform average 10% better in Revised JNLPBA than in JNLPBA. Moreover, the cross-validation test is carried out which we train the NER systems on JNLPBA/Revised JNLPBA corpora and access the performance in both protein-protein interaction extraction (PPIE) and biomedical event extraction (BEE) corpora to confirm that the newly refined Revised JNLPBA is a competent NER corpus in biomedical relation application. The revised JNLPBA corpus is freely available at ***/BNER/Content/Revised_***. Copyright © 2019, The Authors. All rights reserved.

关键词： Proteins

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Innovations in Social Informatic: A Patent Landscape Review

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Procedia computer science 2024年 234卷 1228-1235页

作者： Nur Afia Agung Purnomo Fairuz Iqbal Maulana Yogi Tri Prasetyo Ma. Janice J. Gumasing Elsa Rosyidah Satria Fadil Persada Institute for Research and Community Services Universitas Nahdlatul Ulama Sidoarjo Sidoarjo 61234 Indonesia English Language Education Universitas Nahdlatul Ulama Sidoarjo Sidoarjo 61234 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department Bina Nusantara University Jakarta 11480 Indonesia School of Industrial Engineering and Engineering Management Mapúa University Manila 1002 Philippines International Bachelor Program in Engineering Yuan Ze University Taoyuan 32003 Taiwan

Patent intellectual property in social informatics with entrepreneurship support creates greater opportunities to improve human life. It has the potential to open enormous innovation opportunities in various fields, such as technology, health, services, and society. This research was conducted to review the patent landscape and the main trends of patents on social informatics in all countries. Researchers applied a patent landscape analysis using data on 487 patent documents over 16 years from the *** database. The study results show yearly patented development patterns related to social informatics. There is a royalty-free opportunity by using 15 patent discontinued. Patent related to social informatics was dominated by company ownership and inventors from the United States. The patent physics (G) section overwhelmed patents related to social informatics. There were 334 simple family patents on social informatics in nine jurisdictions; the United States was the highest.

关键词： entrepreneurship, patent, social informatics innovation

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Effects of incorporating a two-tier assessment-based guiding mechanism into contextual games on students’ learning achievement and behaviors in horticultural education

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Interactive Learning Environments 2024年第10期32卷 6845-6867页

作者： Hwang, Gwo-Jen Lin, An-Chi Chang, Shao-Chen Graduate Institute of Educational information and Measurement National Taichung University of Education Taichung Taiwan Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taipei Taiwan Yuan Ze University Taoyuan Taiwan International bachelor program in informatics department of information communication Yuan Ze University Taoyuan Taiwan

Digital games have been used in various disciplines to enhance students’ learning interest and effectiveness through the gaming contexts. However, most of the digital educational games use multiple-choice questions to confirm students’ learning status, implying the challenge of understanding the actual learning status of students. Also, the gaming system cannot analyze the possible reasons for students’ wrong answers and so provide suggestions. Therefore, this study proposed a two-tier assessment-based contextual gaming (TTA-CG) approach to confirm whether students correctly answer the questions based on the right reasons as well as identifying the reasons for students’ wrong answers. To evaluate the effectiveness of the proposed approach, a quasi-experiment was conducted in a horticulture course. Two classes of first-year vocational high school students were recruited as the participants. One class with 35 students was the experimental group learning with the TTA-CG approach;the other class with 35 students was the control group adopting the conventional contextual gaming (C-CG) approach. The experimental results showed that the TTA-CG approach was more effective than the C-CG approach in terms of enhancing students’ learning achievement, motivation, and self-efficacy. In addition, there was no significant difference between the two groups in terms of cognitive load. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Contextual game-based learning learning achievement learning behavior analysis plant identification two-tier assessment

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Statistical Dependence in Biological Sequences

Statistical Dependence in Biological Sequences

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IEEE international Symposium on Information Theory

作者： Hasan Metin Aktulga Ioannis Kontoyiannis L. Alex Lyznik Lukasz Szpankowski Ananth Y. Grama Wojciech Szpankowski Department of Computer Scienc Purdue University West Lafayette IN U.S.A. Department of Informatics Athens University of Econ Business Athens Greece Department of Informatics Athens Univ of Econ & Business Athens Greece Pioneer Hi-Bred International Johnston IA USA Bioinformatics Program UCSD San Diego CA U.S.A. Department of Computer Science Purdue University West Lafayette IN U.S.A.

ISBN: (纸本)9781424413973;1424413974

We demonstrate the use of information-theoretic tools for the task of identifying segments of biomolecules (DNA or RNA) that are statistically correlated. We develop a precise and reliable methodology, based on the notion of mutual information, for finding and extracting statistical as well as structural ependencies. A simple threshold function is defined, and its use in quantifying the level of significance of dependencies between biological segments is explored. These tools are used in two specific applications. First, for the identification of correlations between different parts of the maize zmSRp32 gene. There, we find significant dependencies between the 5' untranslated region and its alternatively spliced exons. This observation may indicate the presence of as-yet unknown alternative splicing mechanisms or structural scaffolds. Second, using data from CODIS, we demonstrate that our approach is well suited for the problem of discovering short tandem repeats (STRs).

关键词： RNA Mutual information Sequences DNA Splicing Protein engineering Molecular biophysics computer science Bioinformatics Information analysis

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Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

arXiv

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arXiv 2023年

作者： Chiu, Ching-Yu Müller, Meinard Davies, Matthew E.P. Su, Alvin Wen-Yu Yang, Yi-Hsuan The Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan The International Audio Laboratories Erlangen Germany The Department of Informatics Engineering Centre for Informatics and Systems of the University of Coimbra University of Coimbra Portugal The Department of Computer Science and Information Engineering National Cheng Kung University Taiwan Yating Music Team Taiwan AI Labs Taiwan Research Center for IT Innovation Academia Sinica Taiwan

To model the periodicity of beats, state-of-the-art beat tracking systems use "post-processing trackers" (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called "predominant local pulses" (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838). © 2023, CC BY.

关键词： Dynamic programming

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Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River

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Engineering Applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Li, Shuguang Qasem, Sultan Noman Band, Shahab S. Ameri, Rasoul Pai, Hao-Ting Mehdizadeh, Saeid School of Computer Science and Technology Shandong Technology and Business University Yantai China Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management National Yunlin University of Science and Technology Douliu Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan Water Engineering Department Urmia University Urmia Iran

Monitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentration of the dissolved oxygen (DO) is one of the most significant indicators of the quality of river water. The current study aimed to estimate the minimum, maximum, and mean DO concentrations (DO min, DO max, DO mean) at a gauging station located on Tualatin River, United States. To that end, four machine learning models, such as support vector regression (SVR), multi-layer perceptron (MLP), random forest (RF), and gradient boosting (GB) were established. Root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and Nash-Sutcliffe efficiency (NSE) metrics were employed to better assess the accuracies of these models. The modeling results demonstrated that the SVR and MLP surpassed the RF and GB models. Despite this, the SVR was concluded to be the best-performing method when used to estimate the DO min, DO max, and DO mean. The best error statistics in the testing phase were related to the SVR model with full (four) inputs to estimate DO mean concentration (RMSE = 0.663 mg/l, MAE = 0.508 mg/l, R = 0.945, NSE = 0.875). Finally, the explainability of the superior models (i.e. SVR models) was conducted using SHapley Additive exPlanations (SHAP) for the first time to estimate DO concentration. In fact, evaluating the explainability of machine learning models can provide useful information about the impact of each of the input estimators used in the procedure of models development. It was concluded that the specific conductance (SC) and followed by water temperature (WT) could provide the most contributions for estimating the DO min, DO max, and DO mean concentrations. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： dissolved oxygen concentration estimation Explainable machine learning SHapley additive explanations

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A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors

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computers and Education: Artificial Intelligence 2020年 1卷

作者： Hwang, Gwo-Jen Sung, Han-Yu Chang, Shao-Chen Huang, Xing-Ci Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology 43 Sec.4 Keelung Rd. Taipei106 Taiwan Department of Allied Health Education and Digital Learning National Taipei University of Nursing and Health Sciences No.365 Ming-te Road Peitou District Taipei City Taiwan Department of International Bachelor Program in Informatics and the Department of Information Communication Yuan Ze University No. 135 Yuandong Rd. Zhongli Dist. Taoyuan City320 Taiwan

Scholars have indicated the importance of providing guidance and support for individual learners. In the past decades, most studies have developed adaptive learning systems to address this issue mainly based on students’ cognitive status, such as their learning achievements. However, several educators have pointed out the need to take learners’ affective status into account. Therefore, this study proposed an expert system approach by taking into account both the affective and cognitive status of individual learners. An adaptive learning system was implemented based on the proposed approach. In addition, an experiment was conducted in a fifth-grade Mathematics course to compare the learning performances and perceptions of the students who learned with the adaptive learning system with affective and cognitive status analysis, a cognitive-status-based adaptive learning system, and a conventional learning system. The ANCOVA results revealed that the adaptive learning model with the affective and cognitive performance analysis mechanism outperformed the other two approaches in terms of improving the students’ learning achievement (F = 3.12, p © 2020 The Author(s)

关键词： Expert systems

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VIRTUAL PROTOTYPING USING KNOWLEDGE - BASED MODELING AND SIMULATION TECHNIQUES

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NAVAL ENGINEERS JOURNAL 1993年第3期105卷 201-212页

作者： SHEA, JG The Author:holds bachelor and master of engineering degrees in mechanical engineering a M.Eng. in engineering management and is currently fulfilling requirements for the M.S. and Ph.D. degrees in computer science at the University of Louisville. He is employed as program manager Phalanx Advanced Engineering Development at the Naval Ordnance Station Crane Div. NavSurfWarCen Louisville Ky. During his tenure with Phalanx Mr. Shea has contributed to system reliability improvement system performance upgrading and the development of the Phalanx HOL (RISC) Computer. Mr. Shea is a member of ASNE the Institute of Electrical and Electronics Engineers American Institute of Aeronautics and Astronautics Society for Computer Simulation International Test & Evaluation Association and the Military Operations Research Society.

Knowledge-based modeling and simulation bridges the gap between ''conventional'' artificial intelligence implementations (such as expert systems) and more traditional computer-aided design techniques. We are currently developing software, whose primary function is to capture a user-input design specification and produce a ''virtual'' rapid prototvpe in the form of executable rule-based code. This code can then be exercised either as an interactive part of a hardware-in-the-loop testbed simulator or as a component of an object-oriented ''behavioral'' simulation environment. While the Phalanx Testbed is the immediate beneficiary of this work, the techniques described have a wide range of application in the modeling of conceptual design and performance characteristics. This paper describes the system architecture and software tools that we are applying to generate virtual rapid prototypes for use in the Phalanx Testbed. Particular attention is paid to defining the intelligent knowledge-capture mechanisms and model generation methodologies that we are using to translate design knowledge and performance requirements into rule-based simulations. The object-oriented programming approach to the merging of ''new'' data with previouslv-captured and stored data is discussed, and the issues of verifying and validating prototypes generated using such partiallv ''reengineered'' models are examined. An application currently in use as an investigative prototype for testbed development, a simple position controller servomechanism used to control the azimuth angle of a target-tracking sensor, is used to illustrate the process.

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