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Large-Scale Radiomics Analysis for Prostate Cancer Detection Harnessing Machine and Deep learning Models 22

Large-Scale Radiomics Analysis for Prostate Cancer Detection...

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22nd IEEE international Symposium on Biomedical Imaging, ISBI 2025

作者： Mylona, Eugenia Zaridis, Dimitrios I. Kalantzopoulos, Charalampos N. Pezoulas, Vasileios C. Tachos, Nikolaos S. Matsopoulos, George K. Regge, Daniele Papanikolaou, Nikolaos Tsiknakis, Manolis Marias, Kostas Fotiadis, Dimitrios I. Biomedical Research Institute Forth Ioannina GR 45110 Greece University of Ioannina Unit of Medical Technology Intelligent Information Systems Greece School of Electrical & Computer Engineering National Technical University of Athens Biomedical Engineering Laboratory Athens 15780 Greece Candiolo Cancer Institute FPO-IRCCS Department of Radiology Strada Provinciale Candiolo Turin 10060 Italy Champalimaud Foundation Computational Clinical Imaging Group Portugal Institute of Computer Science Forth Computational Biomedicine Laboratory Heraklion GR 70013 Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering Heraklion GR 71004 Greece

ISBN: (纸本)9798331520526

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability. © 2025 IEEE.

关键词： Deep learning Lesion Detection Machine learning Prostate Cancer Radiomics

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Deep learning semantic segmentation for indoor terrain extraction: Toward better informing free-living wearable gait assessment

Deep learning semantic segmentation for indoor terrain extra...

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IEEE-EMBS international conference on Wearable and Implantable Body Sensor Networks (BSN) / 18th IEEE-EMBS international conference on Biomedical and Health Informatics (BHI)

作者： Moore, Jason Stuart, Sam Walker, Richard McMeekin, Peter Young, Fraser Godfrey, Alan Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne Tyne & Wear England Northumbria Univ Dept Sport Exercise & Rehabil Newcastle Upon Tyne Tyne & Wear England Northumbria Healthcare NHS Fdn Trust Newcastle Upon Tyne Tyne & Wear England Northumbria Univ Dept Nursing Midwifery & Hlth Newcastle Upon Tyne Tyne & Wear England

ISBN: (数字)9781665459259

ISBN: (纸本)9781665459259

Contemporary approaches to gait assessment use wearable within free-living environments to capture habitual information, which is more informative compared to data capture in the lab. Wearables range from inertial to camerabased technologies but pragmatic challenges such as analysis of big data from heterogenous environments exist. For example, wearable camera data often requires manual time-consuming subjective contextualization, such as labelling of terrain type. There is a need for the application of automated approaches such as those suggested by artificial intelligence (AI) based methods. This pilot study investigates multiple segmentation models and proposes use of the PSPNet deep learning network to automate a binary indoor floor segmentation mask for use with wearable camera-based data (i.e., video frames). To inform the development of the AI method, a unique approach of mining heterogenous data from a video sharing platform (YouTube) was adopted to provide independent training data. The dataset contains 1973 image frames and accompanying segmentation masks. When trained on the dataset the proposed model achieved an Instance over Union score of 0.73 over 25 epochs in complex environments. The proposed method will inform future work within the field of habitual free-living gait assessment to provide automated contextual information when used in conjunction with wearable inertial derived gait characteristics. Clinical Relevance-Processes developed here will aid automated video-based free-living gait assessment.

关键词： Deep learning Gait Analysis IMU Terrain Classification Floor Segmentation

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HMPD: A Novel dataset for Microplastics Classification with Digital Holography 1

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22nd international conference on Image Analysis and Processing (ICIAP)

作者： Cacace, Teresa Del-Coco, Marco Carcagni, Pierluigi Cocca, Mariacristina Paturzo, Melania Distante, Cosimo Natl Res Council Italy Inst Appl Sci & Intelligent Syst ISASI Pozzuoli Italy Natl Res Council Italy Inst Appl Sci & Intelligent Syst ISASI Lecce Italy Natl Res Council Italy Inst Polymers Composites & Biomat Pozzuoli Italy

ISBN: (数字)9783031431531

ISBN: (纸本)9783031431524;9783031431531

Microfiber shedding caused by the washing of synthetic textiles is widely recognized as a major source of environmental microplastic pollution. Such a phenomenon pushed the scientific community to develop ever better methodologies for the detection and the identification of synthetic microfibers dispersed in water involving, mainly because of the microscopic nature of the elements under analysis, a wide range of competencies ranging from instrumental acquisition to data analysis. This work deploys a large dataset of holographic images of microplastic and non-microplastic samples carefully labeled by experts. data acquisition starts from video sequences collected by means of a holographic microscope recording the flow of water samples;then, holography post-processing and object detection are performed to retrieve the patches of interest. Finally, classification tests exploiting deep learning strategies have been performed to validate the proposed dataset. The dataset and the benchmarking code are available at: https://***/beppe2hd/ HMPD.

关键词： dataset microplastic holography deep learning

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An EfficientNet-Based Transfer learning System for Defect Classification in Manufacturing

An EfficientNet-Based Transfer Learning System for Defect Cl...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Muhammad Rashid Rasheed Sonya Coleman Bryan Gardiner Philip Vance Cormac McAteer Khoi Nguyen Intelligent Systems Research Centre Ulster University Londonderry U.K Seagate Technology Springtown Industrial Estate Londonderry U.K

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

In semiconductor manufacturing industry, automated systems are essential for efficient and accurate identification of defects, prior to final product completion, to ensure quality and reduce waste. To achieve this, semiconductor industries are developing smart inspection systems to identify defects on the surface of wafers during manufacturing. Computer vision techniques play a crucial role in developing accurate inspection systems. However, most existing computer vision-based systems perform poorly when classifying defects, and many manufacturing companies still rely on manual inspection. To overcome this, we propose an efficient method for classifying defects in an industrial dataset using EfficientNet-B4 transfer learning along with Squeeze and Excitation block and multilayer perceptron. Furthermore, we utilise the various data-augmentation techniques to enrich the dataset and improve the generalisation of proposed model. This proposed method is lightweight and can classify defects in real-time with an accuracy of approximately 98%.

关键词： Semiconductor device modeling Accuracy Transfer learning Manuals Inspection Semiconductor device manufacture Multilayer perceptrons Real-time systems Manufacturing Informatics

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End-to-End Deep learning for Detecting Metastatic Breast Cancer in Axillary Lymph Node from Digital Pathology Images 22nd

End-to-End Deep Learning for Detecting Metastatic Breast Can...

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22nd international conference on intelligent data engineering and automated learning

作者： Turki, Turki Al-Sharif, Anmar Taguchi, Y-H King Abdulaziz Univ Dept Comp Sci Jeddah 21589 Saudi Arabia Chuo Univ Dept Phys Tokyo 1128551 Japan

ISBN: (纸本)9783030916077;9783030916084

Metastatic breast cancer is one of the attributed leading causes of women deaths worldwide. Accurate diagnosis to the spread of breast cancer to axillary lymph nodes (ALNs) is done by breast pathologist, utilizing the microscope to inspect and then providing the biopsy report. Because such a diagnosis process requires special expertise, there is a need for artificial intelligence-based tools to assist breast pathologists to automatically detect breast cancer metastases. This study aims to detect breast cancer metastasized to ALN with end-to-end deep learning (DL). Also, we utilize several DL architectures, including DenseNet121, ResNet50, VGG16, Xception as well as a customized lightweight convolutional neural network. We evaluate the DL models on NVIDIA GeForce RTX 2080Ti GPU using 114 processed microscopic images pertaining to ALN metastases in breast cancer patients. Of note, we included in the evaluation several machine learning (ML) algorithms coupled with histogram features, extracted from the same studied images. These ML include Random Forest, AdaBoost and SVM. Compared to all models employed in this study, experimental results show that DenseNet121 generates the highest performance results based on measures used in class-imbalance problems.

关键词： Breast cancer Axillary lymph node status Computational pathology Artificial intelligence Deep learning

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Evaluating Football Player Actions During Counterattacks 22nd

Evaluating Football Player Actions During Counterattacks

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22nd international conference on intelligent data engineering and automated learning

作者： Raudonius, Laurynas Allmendinger, Richard Univ Manchester Manchester Lancs England

ISBN: (纸本)9783030916077;9783030916084

Evaluation of actions made by footballers has a wide range of applications in the field of football analytics as it would help compare players and consequently aid scouting, team selection and many other aspects of professional football. Although there is a substantial amount of research aiming to solve this particular problem, most existing research in this domain focuses on just one aspect of the game, namely ball passes. Even though passes are a vital part of football, they do not provide a holistic view on evaluating player actions. This project presents a model that paints a more complete picture and values player contributions (dribbles and passes) to counterattacks by employing four separate performance indicators (which are then combined into one output): distance, danger, outplayed players and space control. The model was tested and optimised on datasets of 10 matches that were provided by the Stats Perform group. It was found that the four indicators are generally complementary in that they tell us different information about how player actions impacted the counterattack. Another finding of this project is that the way separate indicators and combined scores are distributed closely models the distribution of footballers based on their quality most are average and the better they get, the less of them there are. Finally, we discuss a use case of the proposed model for player recruitment.

关键词： Association football Counterattacks Positional data analysis Player action valuation

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Person Identification with Voice and Ear-print in Degraded Conditions for Smart Home Access 22

Person Identification with Voice and Ear-print in Degraded C...

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22nd international conference on Cyberworlds, CW 2023

作者： Boujnah, Sana Ferjaoui, Radhia Khalifa, Anouar Ben University of Tunis El Manar National Engineering School of Tunis Tunisia NOCCS-Laboratory of Networked Objects Control and Communication Systems Eniso Tunisia Istmt Tunis Tunisia Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Université de Jendouba Institut National des Technologies et des Sciences du Kef Le Kef7100 Tunisia

ISBN: (纸本)9798350315653

Since the smartphone is adapted to the ambient intelligence and the smart home systems are remotely accessed through the smartphones, there is a need for a secure authentication system based on some biometrics proprieties that can be taken from a smartphone. The identification of persons through ear and voice print is one of the basic biometric matters. The earlier research in ear recognition have shown that human ear is one of the representative human biometrics with uniqueness and stability. Indeed, the human voice is a perfect source of data for person identification in many applications. In this paper, we propose a fusion between the ear and voice biometrics in degraded conditions in a smart home context at 3 levels (feature, score, and decision). The experiments are conducted on the EVDDC database and a chimeric database (TIMIT and USTB-I). The best results are obtained with the feature level fusion (95.8%) with the KNN classifier. © 2023 IEEE.

关键词： Biometrics

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Architecting Digital Twins for intelligent Transportation Systems

Architecting Digital Twins for Intelligent Transportation Sy...

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IEEE international conference on Software Architecture Workshops (ICSAW)

作者： Hiya Bhatt Sahil Karthik Vaidhyanathan Rahul Biju Deepak Gangadharan Ramona Trestian Purav Shah Software Engineering Research Centre IIIT Hyderabad India Computer Systems Group IIIT Hyderabad India Faculty of Science and Technology Middlesex University London

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for intelligent Transportation Systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data. To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency.

关键词： Adaptation models Software architecture Transportation Computer architecture Predictive models Traffic control Real-time systems Digital twins Safety Predictive analytics

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Inception Model for Malaria Detection Using Malaria Cell Images dataset

Inception Model for Malaria Detection Using Malaria Cell Ima...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Shikha Prasher Leema Nelson S. Gomathi Chitkara University Institute of Engineering & Technology Chitkara University Punjab India Saveetha School of Engineering SIM ATS Chennai

In this study, deep learning models were used to perform binary classification of malaria-infected and uninfected human skin cells. The study compares the efficacy of four different models, namely Convolutional Neural Network (CNN), Inception, ResNet, and VGG19, in detecting malaria. The findings through experimentation and analysis show that the Inception model beats its competitors, obtaining the greatest accuracy rate of 94.5%• This high precision score demonstrates the efficacy of Inception's novel design, which includes inception modules for simultaneous multi-scale feature extraction. While CNN, ResNet, and VGG19 all perform admirably, the Inception model's greater accuracy emphasizes its potential as a robust method for the automated detection of malaria-infected cells in microscopic images. It helps in biomedical. it also helps to improve quality of life.

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Exploiting Artificial Intelligence Approaches to Augment Decision-making Processes for an intelligent Performance in Football

Exploiting Artificial Intelligence Approaches to Augment Dec...

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learning and Technology conference (L&T)

作者： Souheila Boudouda Aya Merouani Naila Marir LIRE Laboratory University of Abdelhamid Mehri Constantine Algeria Effat College of Engineering Effat University Jeddah Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

The world of Football has witnessed remarkable advancements, elevating the sport from being merely "The Beautiful Game" to a realm where numbers and data hold immense significance. While some clubs and teams have achieved success through research, there is a pressing need for further investment in research, particularly with the integration of Artificial Intelligence (AI) technologies. AI has the potential to revolutionize team performance analysis by providing real-time insights and facilitating the analysis of instant games. An illustrative instance of the application of reinforcement learning for the purpose of analyzing player styles and conducting comparative assessments among various players can significantly enhance the simulation of these players within video games, such as FIFA games. Furthermore, it can augment the process of recruiting new players in accordance with the distinct training methodologies employed by individual clubs within diverse *** this paper, we propose a novel approach to developing an intelligent system that can analyze and predict Football players' performances in real-time. Our approach utilizes machine learning and deep learning techniques, as well as a recommendation system, to comprehensively analyze players' performance. This proposed system aims to support managers and players in making informed decisions by providing them with valuable insights based on diverse data sources. By leveraging this system, teams can enhance their performance and potentially attain higher international rankings.

关键词： Training Video games Soft sensors Games Reinforcement learning Pressing Real-time systems Artificial intelligence Recommender systems Sports

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