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Medical Imaging 2025: Clinical and Biomedical Imaging

作者： Samir, Jessica Ramadass, Karthik Saunders, Adam M. Krishnan, Aravind R. Remedios, Lucas W. McMaster, Elyssa M. Landman, Bennett A. Dept. of Psychological Science and Neuroscience Belmont University NashvilleTN United States Dept. of Computer Science NashvilleTN United States Dept. of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center NashvilleTN United States

ISBN: (纸本)9781510685987

Segmentation of anatomical structures leverages medical imaging technology for accurate diagnosis and treatment planning. Recent advances in active learning offer the opportunity for the interactive creation of artificial intelligence (AI) segmentation algorithms. Although active learning has been extensively investigated retrospectively, prospective studies have not been widely reported. This study aimed to determine whether a novice in medical imaging could independently label anatomical structures using Radiopaedia, 20% of clinician-generated labels for education of the novice, and AI guidance. Given the pancreas and spleen challenges from the Medical Segmentation Decathlon (MSD), a novice employed the MONAI Label extension on 3D Slicer with active learning (AL) techniques to perform segmentation. This study also explored the use of MONAI (Medical Open Network for AI) Label, a user-friendly framework for label submission and model training. Our segmentation results yielded Dice scores comparable to the range of those from the 2018 segmentation challenges of the MSD. We achieved Dice scores of 0.209 for the pancreas task using 70 images and 0.831 for the spleen using 34 images. The pancreas results do not fall within MSD’s reported ranges for the L1 region which is 0.48-0.80. However, the spleen results fall within the MSD’s reported range of 0.83-0.97. The interactive AI framework by MONAI Label effectively segments as we reached reasonable performance in less than a month with one person without clinical expertise. © 2025 SPIE.

关键词： Medical education

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Unpaired Shadow Removal: Enhancing Attention to Shadow Areas via Dropkey

Unpaired Shadow Removal: Enhancing Attention to Shadow Areas...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Yang, Jaewon Shin, Ukcheol Cho, Donghyeon Chungnam National University Dept. of Electronics Engineering Daejeon Korea Republic of Robotics Institute Carnegie Mellon University Pittsburgh United States Hanyang University Dept. of Computer Science Seoul Korea Republic of

ISBN: (纸本)9798331510756

Shadows appear in image areas where an object obstructs the light path. These areas, having lower values than non-shadow areas, degrade image quality and lead to issues in object recognition and segmentation. Supervised shadow removal depends on datasets with shadow and shadow-free image pairs, sparking increased interest in unpaired techniques. However, unpaired shadow removal faces challenges in accurately focusing on shadow areas without direct supervisory signals from ground truth. Consequently, shadows are not effectively removed, and non-shadow areas may suffer from unintended distortions. In this paper, we introduce a transformer-based network designed to identify shadow areas accurately, leveraging global context through spatial and channel attention mechanisms. Additionally, in the training phase, the transformer network is trained to precisely concentrate on shadow areas using a domain classifier and a dropkey mechanism, which randomly drops features of the keys to enhance focus. Our method is tested across several benchmark datasets for shadow removal, such as ISTD, ISTD+, SRD and WSRD, demonstrating better performance compared to current unpaired approaches. © 2025 IEEE.

关键词： Image segmentation

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Breaking Tester Stereotypes: who is testing and why it matters 24

Breaking Tester Stereotypes: who is testing and why it matte...

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Proceedings of the 37th International BCS Human-computer Interaction Conference

作者： Isabel Evans Chris Porter Mark Micallef Dept. of Computer Information Systems Faculty of ICT University of Malta Msida Malta Dept. of Computer Science

Testing is essential for successful delivery of software solutions and is not always performed by specialist testers. In earlier studies, we noted a wide diversity in the backgrounds of the testers participating in these studies, prompting our research question: Who is Testing? We conducted a qualitative study of over 70 industry testers, covering testers from multiple countries and domains, with information about their backgrounds, hobbies, roles and characteristics. We show testers are from a wide range of backgrounds, with differing needs, characteristics, and problem-solving preferences. Their roles in software projects are multi-faceted, requiring a high cognitive skill level. We discuss how to break stereotyping and best support diversity in testers’ backgrounds and *** consider whether software testers are different from other software practitioners, and how understanding tester personas helps support of testers and testing.

关键词： Software Testing

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Characterization and Statistical Analysis of Beer Produced by Bacterial Strains Isolated from Traditional Fermented Foods of Northeast India 14

Characterization and Statistical Analysis of Beer Produced b...

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14th IEEE International Conference on Communication systems and Network Technologies, CSNT 2025

作者： Borthakur, Debashree Chowdhury, Ranbir Sharma, Bipin Kumar Dept. of Microbiology Tripura University Tripura India Dept. of Bio Sciences Assam Don Bosco University Assam India Dept. of Computer Science and Engineering Assam Don Bosco University Assam India

ISBN: (纸本)9798331531935

Beer fermentation is a multifaceted biochemical process influenced by microbial activity, with yeasts and lactic acid bacteria (LAB) playing pivotal roles in determining its sensory attributes. This study aims to isolate and characterize LAB strains from traditional fermented foods of Northeast India and evaluate their potential in enhancing the sensory properties of beer. A total of 41 LAB strains were isolated and subjected to biochemical characterization, including proteolytic, lipolytic, and carbohydrate fermentation assays. Non-pathogenic strains (B/4, Chuwan/YEPD/1, HTK/5, Khorisa/NA/3, NAP/2, NGA/5, NGA/6, Soibum/NA/3, TL/2, TL/4, TL/5 and TL/6) exhibiting favorable metabolic activities were selected for beer fermentation using a rice-based substrate. Sensory evaluation was conducted by a trained panel to assess attributes such as aroma, flavor, mouthfeel, and overall acceptability. Statistical analyses, including the Friedman and Nemenyi tests, revealed significant variations in the sensory profiles of beer samples produced by different LAB strains. Notably, the strains (B/4, Chuwan/YEPD/1, HTK/5, Khorisa/NA/3, NAP/2, NGA/5, NGA/6 and TL/2) imparted distinctive aroma and flavor, indicating its potential application in specialty beer production. These findings underscore the role of LAB in modulating beer characteristics and offer valuable insights for the development of innovative, naturally fermented beverages. These findings highlight the potential application of LAB strains in specialty beer production, emphasizing their role in enhancing sensory appeal. While the study confirms LAB’s viability in small-scale brewing, further research involving large-scale trials and advanced metabolomic analyses is recommended to assess their full industrial potential. This study provides valuable insights into the use of indigenous LAB strains for developing innovative, naturally fermented beverages. © 2025 IEEE.

关键词： Beer

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Investigation of the Coupling Characteristics of Wheel Polygon and Rail Corrugation in High-Speed Railways 3

Investigation of the Coupling Characteristics of Wheel Polyg...

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3rd International Conference on Rail Transportation, ICRT 2024

作者： Gao, Wanlin Yin, Yue Bu, Han Wang, Shiqi Cui, Xiaolu Dept. of Computer Science and Technology Chongqing Jiaotong Univ. Chongqing China Dept. of Rail Transit Chongqing Vocational College of Transportation Research State Univ. Chongqing China Dept. of Mechanical and Electrical Engineering and Vehicle Engineering Chongqing Jiaotong Univ. Chongqing China

ISBN: (数字)9780784485941

ISBN: (纸本)9780784485941

Wheel polygon and rail corrugation are common types of periodic wear in the wheel-rail system. This research investigates their interaction in high-speed railways using friction coupling vibration theory. A finite element model of the wheel-rail system is developed to study friction coupling vibrations in straight sections of long slopes. The study identifies the causes of 22nd-order wheel polygonization and rail corrugation with a 130 mm wavelength and creates wear models for both. The effects of their interaction on friction coupling vibrations are analyzed, with an emphasis on frequency-dependent coupling characteristics. Results indicate that saturated wheel-rail creep forces in long slopes easily trigger friction coupling vibrations, which are worsened by both wheel polygon and rail corrugation, with wheel polygon having a more pronounced impact. These findings offer insights for improving wheel-rail system design and maintenance in high-speed railways. Future research should aim to develop advanced techniques to mitigate the negative effects of wheel polygon and rail corrugation on system performance and safety. © ASCE.

关键词： Railroads

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Comparative Analysis of Deep Learning Models for Structural Defect Segmentation in Bridges

Comparative Analysis of Deep Learning Models for Structural ...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： O'Neal, Kiara Hu, Da Kennesaw State University Dept. of Computer Science MariettaGA United States Kennesaw State University Dept. of Civil and Environmental Engineering MariettaGA United States

ISBN: (纸本)9798331504847

Automated structural defect detection is essential for ensuring the safety and maintenance of civil infrastructure, particularly in bridges where defects such as cracks, spalling, and corrosion can compromise structural integrity. This paper presents a comparative study of three semantic segmentation models - U-Net, Feature Pyramid Network (FPN), and DeepLabv3+ - for detecting and classifying structural defects in bridge imagery. Each model was evaluated using two encoder architectures, EfficientNet B3 and MobileOne S4, to assess the impact of different feature extraction strategies on segmentation accuracy. The experiments were conducted using the DACL benchmark dataset, which includes a wide range of defect classes. FPN paired with EfficientNet B3 demonstrated the highest mean accuracy across most defect categories, outperforming the other combinations, particularly in detecting common defects such as cracks and graffiti. However, certain defect types, such as hollow areas and cavities, presented challenges for all models. These results highlight the effectiveness of deep learning models in automated defect detection, while also identifying areas where further refinement is needed to improve performance in more complex defect scenarios. © 2025 IEEE.

关键词： Semantic Segmentation

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Event Identification and Chronology Model of Ramayana

Event Identification and Chronology Model of Ramayana

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International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Apurba Paul Rishabh Mishra Nayan Padia Dipankar Das Dept. of Computer Science & Engineering IEM Kolkata UEM Kolkata Kolkata India Dept. of Computer Science & Engineering IEM Kolkata Kolkata India Dept. of Computer Science & Engineering Jadavpur University Kolkata India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

The paper introduces an event chronology model for Hindu mythological texts, with a focus on the Ramayana's Bala-kanda. It captures and structures sentence-level events based on their chronological order to better understand narrative progression and character interactions. A manually crafted dataset of 1217 events, involving 214 characters across 75 chapters, is used to generate a knowledge graph. The graph organizes events by sequence, linking characters, actions, and chapters within the storyline. The event chronology is mapped by establishing relationships between events, ensuring they are connected in the proper sequence to reflect the narrative flow. This approach enables a deeper understanding of how each event contributes to the overarching story, providing insights into both the individual actions of characters and their significance within the broader mythological narrative. Transformer-based models (BERT, DistilBERT, and RoBERTa) were utilized for predicting subsequent events, with RoBERTa showing the highest accuracy in preserving the correct event chronology. This structure is key to analyzing the sequential development of the Ramayana's storyline.

关键词： Accuracy Annotations Refining Knowledge graphs Manuals Predictive models Transformers Guidelines

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PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier

PhishVQC: Optimizing Phishing URL Detection with Correlation...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Md. Farhan Shahriyar Gazi Tanbhir Abdullah Md Raihan Chy Mohammed Abdul Al Arafat Tanzin Md. Jisan Mashrafi Dept. of Computer Science and Engineering World University of Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Southern University Bangladesh Chittagong Bangladesh Dept. of Computer Science and Engineering Brac University Dhaka Bangladesh

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Phishing URL detection is crucial in cybersecurity as malicious websites disguise themselves to steal sensitive information. Traditional machine learning techniques struggle to perform well in complex real-world scenarios due to large datasets and intricate patterns. Motivated by quantum computing, this paper proposes using Variational Quantum Classifiers (VQC) to enhance phishing URL detection. We present PhishVQC, a quantum model that combines quantum feature maps and variational ansatzes such as RealAmplitude and EfficientSU2. The model is evaluated across two experimental setups with varying dataset sizes and feature map repetitions. PhishVQC achieves a maximum macro average F1-score of 0.89, showing a 22% improvement over prior studies. This highlights the potential of quantum machine learning to improve phishing detection accuracy. The study also notes computational challenges, with execution wall times increasing as dataset size grows.

关键词： Uniform resource locators Quantum computing Correlation Accuracy Phishing Computational modeling Machine learning Feature extraction Intelligent systems

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PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier

arXiv

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arXiv 2025年

作者： Farhan Shahriyar, Md. Tanbhir, Gazi Chy, Abdullah Md Raihan Tanzin, Mohammed Abdul Al Arafat Jisan Mashrafi, Md. Dept. of Computer Science and Engineering World University of Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Southern University Bangladesh Chittagong Bangladesh Dept. of Computer Science and Engineering Brac University Dhaka Bangladesh

—Phishing URL detection is crucial in cybersecurity as malicious websites disguise themselves to steal sensitive information. Traditional machine learning techniques struggle to perform well in complex real-world scenarios due to large datasets and intricate patterns. Motivated by quantum computing, this paper proposes using Variational Quantum Classifiers (VQC) to enhance phishing URL detection. We present PhishVQC, a quantum model that combines quantum feature maps and variational ansatzes such as RealAmplitude and EfficientSU2. The model is evaluated across two experimental setups with varying dataset sizes and feature map repetitions. PhishVQC achieves a maximum macro average F1-score of 0.89, showing a 22% improvement over prior studies. This highlights the potential of quantum machine learning to improve phishing detection accuracy. The study also notes computational challenges, with execution wall times increasing as dataset size grows. Copyright © 2025, The Authors. All rights reserved.

关键词： Phishing

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Federated Learning in Healthcare: A Privacy-Preserving Approach to Predictive Analytics

Federated Learning in Healthcare: A Privacy-Preserving Appro...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Reddy, M. Jithender Kotiyal, Arnav Lakhanpal, Sorabh Badhoutiya, Arti Mounika, T. Jayanthi, Muthuswamy Vasavi College of Engineering Dept. of Computer Science and Engineering Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Punjab Phagwara India GLA University Department of Electrical Engineering Mathura India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798331512965

Technology such as machine learning in healthcare has pushed privacy concerns to the point that the handling of sensitive patient data has become paramount. In the context of FL, predictive analytics without data sharing between institutions can be performed through the process. Next, we explore the application of FL in healthcare in order to maintain data privacy while obtaining robust predictive models. FL utilizes aggregated decentralized data from multiple healthcare providers to reduce the chance of data breaches and continue to be compliant with rigorous regulatory frameworks, like HIPAA and GDPR, while doing so. In this work, we evaluate the model performance of FL models in predicting different types of health outcomes in comparison with traditional centralized models. We show that FL can achieve similar predictive accuracy while maintaining data privacy, which makes the choice of FL as a viable alternative to privacy sensitive healthcare environments. Second, applying FL is very challenging: there are communication overhead, data heterogeneity, and security risks. This study also considers ways to minimize these challenges. The results demonstrate the promise of Federated Learning as an innovative paradigm for applying predictive analytics in healthcare, indicative of a robust shift toward more secure, efficient and privacy preserving healthcare systems. © 2025 IEEE.

关键词： Federated learning

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