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IEEE International Symposium on Biomedical Imaging

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

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

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.

关键词： Deep learning Support vector machines Analytical models Biological system modeling Feature extraction Transformers Lesions Prostate cancer Radiomics Principal component analysis

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Level attraction from interference in a two-tone-driven cavity magnonics system

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Physical Review Applied 2025年第1期23卷 014048-014048页

作者： Alan Gardin Guillaume Bourcin Christian Person Christophe Fumeaux Romain Lebrun Isabella Boventer Giuseppe C. Tettamanzi Vincent Castel Quantum and Nano Technology Group School of Chemical Engineering The University of Adelaide Adelaide SA 5005 Australia IMT Atlantique Lab-STICC UMR CNRS 6285 29238 Brest France School of Electrical Engineering and Computer Science The University of Queensland Brisbane QLD 4072 Australia Unité Mixte de Physique CNRS Thales Université Paris-Saclay 91767 Palaiseau France

Coherent and dissipative couplings, respectively characterized by energy level repulsion and attraction, each have different applications for quantum information processing. Thus, a system in which both coherent and dissipative couplings are tunable on demand and in situ is tantalizing. A first step towards this goal is the two-tone driving of two bosonic modes, whose experimental signature was shown to exhibit controllable level repulsion and attraction by changing the phase and amplitude of one drive. However, whether the underlying physics is that of coherent and dissipative couplings has not been clarified, and cannot be concluded solely from the measured resonances (or antiresonances) of the system. Here, we show how the physics at play can be analyzed theoretically. Combining this theory with realistic finite-element simulations, we deduce that the observation of level attraction originates from interferences due to the measurement setup, and not dissipative coupling. Beyond the clarification of the origin of level attraction attributed to interference, our work demonstrates how effective Hamiltonians should be derived to appropriately describe the physics.

关键词： Hamiltonians

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Correction to: Ensemble methods-based comparative study of Landsat 8 operational land imager (OLI) and sentinel 2 multi-spectral images (MSI) for smart farming crop classification (Multimedia Tools and Applications, (2024), 10.1007/s11042-024-19737-0)

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Multimedia Tools and Applications 2024年 1-1页

作者： Gupta, Priyanka Gupta, Prateek Singh, Suraj Kumar Thakur, Bhavna Kumar, Manoj Suresh Gyan Vihar University Jaipur India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Centre for Sustainable Development Suresh Gyan Vihar University Jaipur India School of Agriculture & amp Allied Science DBS Global University Dehradun India School of Computer Science FEIS University of Wollongong in Dubai Dubai Knowledge Park Dubai United Arab Emirates MEU Research Unit Middle East University Amman Jordan

In the original publication of this article, all authors have been mistakenly affiliated with "MEU Research unit, Middle East University, Amman, Jordan." Manoj Kumar is the only author affiliated with this address. The original article has been corrected. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

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Mating versus alternative blood sources as determinants to mosquito abundance and population resilience

arXiv

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arXiv 2024年

作者： Ngwa, Gideon A. Ghakanyuy, Bime M. Teboh-Ewungkem, Miranda I. Banasiak, Jacek Applied Mathematical and Computer Assisted Modelling Unit Department of Mathematics University of Buea Cameroon US Department of Defense Fort MeadeMD United States Department of Mathematics and Applied Mathematics University of Pretoria South Africa Institute of Mathematics Lódź University of Technology Lodz Poland Department of Mathematics and Computer Science University of Bamenda Bamenda Cameroon

A deterministic nonlinear ordinary differential equation model for mosquito dynamics in which the mosquitoes can quest for blood either within a human population or within non-human/vertebrate populations is derived and studied. The model captures both the mosquito’s aquatic and terrestrial forms and includes a mechanism to investigate the impact of mating on mosquito dynamics. The model uses a restricted form of homogeneous mixing based on the idea that the mosquito has a blood-feeding habit by accounting for the mosquitoes’ blood-feeding preferences as well as its gonotrophic cycle. This characterization allows us to compartmentalise the total mosquito population into distinct compartments according to the spatial location of the mosquito (breeding site, resting places and questing places) as well as blood-fed status. Issues of overcrowding and intraspecific competition both within the aquatic and the terrestrial stages of the mosquito’s life forms are addressed and considered in the model. Results show that the inclusion of mating induces bi-stability;a phenomenon whereby locally stable trivial and non-trivial equilibria co-exist with an unstable non-zero equilibrium. The local nature of the stable equilibria is demonstrated by numerically showing that the long-term state of the system is sensitive to initial conditions. The bi-stability state is analogous to the phenomenon of the Allee effect that has been reported in population biology. The model’s results, including the derivation of the threshold parameter of the system, are comprehensively tested via numerical simulations. The output of our model has direct application to mosquito control strategies, for it clearly shows key points in the mosquito’s developmental pathway that can be targeted for control *** Codes 92D30, 92D25, 34C23, 34C60 © 2024, CC BY-NC-ND.

关键词： Nonlinear equations

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Multidimensional clinical data denoising via Bayesian CP factorization

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science China(Technological sciences) 2020年第2期63卷 249-254页

作者： CUI GaoChao ZHU Li GUI LiHua ZHAO QiBin ZHANG JianHai CAO JianTing Graduate School of Engineering Saitama Institute of TechnologySaitama 3690293Japan School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Cognitive Science Department School of Information Science and EngineeringXiamen UniversityXiamen 361005China Tensor Learning Unit Center for Advanced Intelligence ProjectRIKENSaitama 1030027Japan School of Automation Guangdong University of TechnologyGuangzhou 510006China

CANDECOMP/PARAFAC(CP) tensor factorization is an efficient technique for incomplete tensor-data processing through capturing the multilinear latent factors. Based on the incorporate a sparsity-inducing prior over multiple latent factors and appropriate hyper-priors over all hyper-parameters, a Bayesian-based hierarchical probabilistic CP factorization model could be formed. By this way, the rank of the incomplete tensor can be determined automatically. In this paper, we explored the tensor completion method in processing incomplete multidimensional electroencephalogram(EEG) and magnetic resonance imaging(MRI) clinical data. The empirical results indicated that the Bayesian CP tensor factorization of incomplete data method can effectively recover EEG signal with missing data and denoised the noisy MRI data.

关键词： electroencephalogram(EEG) magnetic resonance imaging(MRI) Bayesian tensor factorization CANDECOMP/PARAFAC(CP)

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Carotid Plaque Motion Analysis in Ultrasound Videos to Discover Rupture-Prone Plaque Areas

Carotid Plaque Motion Analysis in Ultrasound Videos to Disco...

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IEEE International Symposium on Biomedical Imaging

作者： Georgia D. Liapi Christos P. Loizou Andrew Nicolaides Maura Griffin Dimitrios Kardoulas Constantinos Pattichis Efthyvoulos Kyriacou Dept. of Electrical Engineering Computer Eng. and Informatics Cyprus University of Technology Limassol Cyprus Vascular Screening and Diagnostic Center Nicosia Cyprus Hemodynamic Unit Vascular Surgery Clinic National and Kapodistrian University of Athens Attikon General University Hospital Athens Greece Dept. of Computer Science University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Identification of carotid plaque motion alterations, throughout the cardiac cycle (CC), may reveal rupture-prone plaque components and assist doctors in ischemic stroke risk stratification. In this study, we investigated Ultrasound (U/S) carotid plaque motion, focusing on middle-systole (MS), the interval between early systole (ES) and peak systole (PS) expecting that the dynamic forces applied on the plaque at the beginning of systole are more profound from cardiac ES to MS, compared to PS. We deployed a computer tool, previously developed by researchers of the e-health laboratory at Cyprus University of technology, for carotid plaque motion estimation in B-mode U/S videos. We included 16 carotid U/S videos (8 Asymptomatic, AS; 8 Symptomatic, SY patients) and performed video frame (VF) resolution standardization, manual region of interest (ROI) selection, identification of 5 CCs (ES and MS pairs), and plaque motion spread and magnitude measurement, based on dense optical flow. The Maximum Angular Spread (MAXFW 20 , first 20% of the plaque’s overall MAXFW; units: 0° to 360°), a measure we created to determine angle differences between motion vectors, in all plaque areas, can assist in classifying the plaque as concordant (low-strain), moderate(m)-discordant (mid-strain) or discordant (highstrain). We identified 1 concordant, 8 m-discordant, and 7 discordant plaques, with MAXFW 20 ranging from 58±7.45°, 91±43.9°, 156±49.2° (mean±standard deviation), respectively. As our primary findings support our hypothesis, in the future, we will also derive the stress applied on the carotid plaque, in ES and MS.

关键词： Ultrasonic imaging Standardization Stroke (medical condition) Vectors Real-time systems Motion measurement Motion analysis

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BENCHMARKING PREDICTIVE CODING NETWORKS – MADE SIMPLE

arXiv

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arXiv 2024年

作者： Pinchetti, Luca Qi, Chang Lokshyn, Oleh Oliviers, Gaspard Emde, Cornelius Tang, Mufeng M’Charrak, Amine Frieder, Simon Menzat, Bayar Bogacz, Rafal Lukasiewicz, Thomas Salvatori, Tommaso Department of Computer Science University of Oxford Oxford United Kingdom Institute of Logic and Computation Vienna University of Technology Vienna Austria MRC Brain Network Dynamics Unit University of Oxford United Kingdom VERSES AI Research Lab Los Angeles United States

In this work, we tackle the problems of efficiency and scalability for predictive coding networks (PCNs) in machine learning. To do so, we propose a library, called PCX, that focuses on performance and simplicity, and use it to implement a large set of standard benchmarks for the community to use for their experiments. As most works in the field propose their own tasks and architectures, do not compare one against each other, and focus on small-scale tasks, a simple and fast open-source library and a comprehensive set of benchmarks would address all these concerns. Then, we perform extensive tests on such benchmarks using both existing algorithms for PCNs, as well as adaptations of other methods popular in the bio-plausible deep learning community. All this has allowed us to (i) test architectures much larger than commonly used in the literature, on more complex datasets;(ii) reach new state-of-the-art results in all of the tasks and datasets provided;(iii) clearly highlight what the current limitations of PCNs are, allowing us to state important future research directions. With the hope of galvanizing community efforts towards one of the main open problems in the field, scalability, we release code, tests, and benchmarks.1 Copyright © 2024, The Authors. All rights reserved.

关键词： Scalability

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Stock Market Trading Based on Market Sentiments and Reinforcement Learning

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computers, Materials & Continua 2022年第1期70卷 935-950页

作者： K.M.Ameen Suhail Syam Sankar Ashok S.Kumar Tsafack Nestor Naglaa F.Soliman Abeer D.Algarni Walid El-Shafai Fathi E.Abd El-Samie Department of Computer Science&Engineering NSS College of EngineeringPalakkad678008KeralaIndia Department of Electronics and Communication Engineering NSS College of EngineeringPalakkad678008KeralaIndia Unitéde Recherche de Matière Condensée D’Electronique et de Traitement du Signal(URAMACETS)Department of PhysicsUniversity of DschangP.O.Box 67DschangCameroon Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Stock market is a place,where shares of different companies are *** is a collection of buyers’and sellers’*** this digital era,analysis and prediction in the stock market have gained an essential role in shaping today’s *** market analysis can be either fundamental or *** analysis can be performed either with technical indicators or through machine learning *** this paper,we report a system that uses a Reinforcement Learning(RL)network and market sentiments to make decisions about stock market *** system uses sentiment analysis on daily market news to spot trends in stock *** sentiment analysis module generates a unified score as a measure of the daily news about *** score is then fed into the RL module as one of its *** RL section gives decisions in the form of three actions:buy,sell,or *** objective is to maximize long-term future *** have used stock data of Apple from 2006 to 2016 to interpret how sentiments affect *** stock price of any company rises,when significant positive news become available in the public *** results reveal the influence of market sentiments on forecasting of stock prices.

关键词： Deep learning machine learning daily market news reinforcement learning stock market

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Automated Food Intake Monitoring System to Prevent Malnutrition Using the Tiago Robot Camera*

Automated Food Intake Monitoring System to Prevent Malnutrit...

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Data science and Engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Fotios S. Konstantakopoulos Daphni K. Plati Labrina A. Pliakou Francesco Scotto Di Luzio Nevio Luigi Tagliamonte Loredana Zollo Manolis Tsiknakis Dimitrios I. Fotiadis Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department University of Ioannina Biomedical Research Institute FORTH Ioannina GR 45110 Greece Research Unit of Advanced Robotics and Human-Centred Technologies Campus Bio-Medico University Rome Italy Institute of Computer Science FORTH Heraklion Crete GR 70013 Greece

The management of a daily diet is a significant concern among individuals in modern culture. The utilization of dietary assessment systems has significantly contributed to the efficient management of malnutrition and dietary habits over a period of time. In order to determine the nutritional value of the food that individuals consume, this study introduces a novel food monitoring system and its architecture. By creating a dataset of food images in a tray and using image processing, machine learning, and computer vision techniques, the nutrients and calories consumed by a patient are calculated. The proposed system achieves a 95.6% Intersection over Union score in the segmentation task, 92.3% top-1 accuracy in the classification task, and 4.6 g mean weight absolute error in the weight estimation *** Relevance: The proposed system has the ability to calculate the nutritional composition, encompassing calories, proteins, fats, and carbs, of food consumed by a patient, making it suitable for nutritional monitoring applications and healthcare systems that aim to monitor malnutrition in hospital patients.

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The Smart Hospital: Data and AI Challenges

The Smart Hospital: Data and AI Challenges

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Data science and Engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Daphni K. Plati Fotios S. Konstantakopoulos Theofanis Kalatzis Dimitrios Manousos Thomas Kassiotis Francesco Scotto Di Luzio Nevio Luigi Tagliamonte Loredana Zollo Manolis Tsiknakis Dimitrios I. Fotiadis Materials Science and Engineering Department Unit of Medical Technology and Intelligent Information Systems University of Ioannina Biomedical Research Institute FORTH Ioannina GR 45110 Greece Institute of Computer Science FORTH Heraklion Crete GR 70013 Greece Research Unit of Advanced Robotics and Human-Centred Technologies Campus Bio-Medico University Rome Italy

Healthcare systems generally constitute a combination of healthcare facilities and providers that collaborate in order to offer a complete range of healthcare services. As the primary providers of healthcare, hospitals hold an essential place in the healthcare system. This study introduces three novel automated Artificial Intelligence (AI) systems by using the robotic platform of the TIAGo robot: (i) an image-based system that monitors the food consumption of the patients and provides information regarding their energy intake to prevent malnutrition; (ii) a rehabilitation system that monitors patients as they perform prescribed exercises to prevent loss of mobility, and (iii) a monitoring system for the correctness of the oxygen therapy in terms of the proper positioning of the oxygen mask to prevent hypoxia complications. A well-designed observational clinical study is planned to evaluate and validate the utility and effectiveness of the proposed AI systems in improving patient stay in the hospital. For the abovementioned systems, three novel datasets were *** Relevance: Through the implementation of AI systems, the proposed research intends to provide specialists with an extra tool to aid in the monitoring of hospitalized patients, thus helping to prepare the way for the Smart Hospital.

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