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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Vasileios S. Loukas Thomas Kassiotis Ignacio Lamata Martinez Lefteris Koumakis Jeroen Bruinsma Roberto Pasciuti Monica Balatresi Ville Tenhunen Adam Fiakkas Lelia Ataliani Georgia S. Karanasiou Manolis Tsiknakis Hannes Hilberger Markus Bödenler Bianca Schnalzer Simone Huber Mattia Pirani Matteo Colombo Sten Hanke Dimitrios I. Fotiadis Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece Biomedical Research Institute FORTH-BRI Ioannina Greece Computational BioMedicine Laboratory Institute of Computer Science FORTH-ICS Heraklion Greece EGI Foundation Amsterdam The Netherlands Dept. of Health Promotion Care and Public Health Research Institute Maastricht University Maastricht The Netherlands ExtraRed Innovations Pisa Italy Infotrend Innovations Co. Ltd Nicosia Cyprus eHealth Institute FH Joanneum University of Applied Sciences Graz Austria Innovation2Grow I2G Bologna Italy

Dementia is the main cause of disability in elderly populations. It has been shown that the risk factors of dementia are a mixture of pathological, lifestyle and heritable factors, with some of those being provably modifiable. Early diagnosis of dementia and approaches to slow down its evolution are currently the most prominent management methodologies due to lack of a cure. For that reason, a plethora of home-based assistive technologies for dementia management do exist, with most of them focusing on the improvement of memory and thinking. The main objective of LETHE is prevention in the whole spectrum of cognitive decline in the elderly population at risk reaching from asymptomatic to subjective or mild cognitive impairment to prodromal Dementia. LETHE will provide a Big Data collection platform and analysis system, that will allow prevention, personalized risk detection and intervention on cognitive decline. Through the subsequent 2-year clinical trial, the LETHE system, as well as the respective knowledge gained will be evaluated and validated. The scope of the current paper is to introduce the LETHE study and its respective novel platform as a holistic approach to multidomain lifestyle intervention trial studies. The present work depicts the architectural perspective and extends beyond state-of-the-art guidelines and approaches to health management systems and cloud platform *** Relevance — Patient Management systems as well as lifestyle management platforms have significant clinical relevance as they allow for remote and continuous monitoring of patients' health status. LETHE aims to improve patient outcomes by providing predictive models for cognitive decline and patient adherence to the multimodal lifestyle intervention, enabling prompt and appropriate medical decisions.

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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models*

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

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

作者： Konstantina-Helen Tsarapatsani Antonis Sakellarios Vasilis D Tsakanikas Henrik Rudolf Hans J. Trampisch Vasileios C. Pezoulas George K. Matsopoulos Dimitrios I. Fotiadis Biomedical Research Institute Foundation for Research and Technology-Hellas (FORTH) Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina GR 45110 Ioannina Greece University Medical Center Rostock Institute for Biostatistics and Informatics in Medicine and Ageing Research Department of Medical Informatics Biometry and Epidemiology Ruhr University Bochum Bochum Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis’s results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided.

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ROBOSTEAMSEN Project - Training SEN Teachers to Use Robotics for Fostering STEAM and Develop Computational Thinking*

ROBOSTEAMSEN Project - Training SEN Teachers to Use Robotics...

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International Symposium on computers in Education (SIIE)

作者： Miguel Á. Conde Francisco J. Rodríguez-Sedano Francisco J. García-Peñalvo Jose Gonçalves Ilkka Jormanainen Andrea Anzanello Jonny Filipe Ribeiro Alves Raquel Fernández Hernáandez Alina- Andreia Ailincai Dept. of Mechanics Computer Science and Aerospace Engineering Universidad de Leon Leon Spain Dept. Electrical Systems and Automation Engineering Universidad de Leon Leon Spain Dpto. de Informatica y Autormatica - IUCE Universidad de Salamanca Salamanca Espana Research Centre in Digitalization and Intelligent Robotics (CeDRI) Instituto Politecnico de Braganca Braganca Portugal School of Computing University of Eastern Finland Joensuu Finland Dept of Project Management Pixel Florence Italy Colegio Internato Dos Carvalhos Carvalhos Portugal Colegio de Educacion Especial Ntra. Sra. del Sagrado Corazon de Leon Leon Spain EuroEd Primary School Lasi Romania

ISBN: (数字)9798350376616

ISBN: (纸本)9798350376623

Our contemporary society necessitates professionals equipped with 21st-century skills. Disciplines within science, Technology, Engineering, Arts, and Mathematics (known as STEAM) have been particularly effective in fostering these skills. However, when considering students with disabilities, especially those with intellectual or developmental disabilities (IDD), this assertion often falls short. In this context, the RoboSTEAMSEN project emerges as an initiative designed to enhance educational processes by providing teachers of IDD students with the necessary resources to promote STEAM engagement. The project proposes the use of active learning methodologies and robotics to achieve this goal. The primary objective of the project is realized through several strategies: understanding the needs of students with disabilities and adapting the use of robotics and active learning methodologies accordingly; training teachers in the use of these resources; and creating a platform to exchange experiences, resources, lessons learned, tools, case scenarios, etc., while reaching other potential stakeholders such as caregivers and policymakers. The main outcomes of the project are teacher training programs and the development of associated competencies, tools to identify and classify resources for the students, and technological platforms to ensure the sustain ability of the project once it concludes.

关键词： Training computers Ecosystems Collaboration Fasteners People with disabilities Mathematics

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Object-centric learning with slot attention 20

Object-centric learning with slot attention

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Proceedings of the 34th International Conference on Neural Information Processing systems

作者： Francesco Locatello Dirk Weissenborn Thomas Unterthiner Aravindh Mahendran Georg Heigold Jakob Uszkoreit Alexey Dosovitskiy Thomas Kipf Dept. of Computer Science ETH Zurich and Max-Planck Institute for Intelligent Systems Google Research Brain Team

ISBN: (纸本)9781713829546

Learning object-centric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep learning approaches learn distributed representations that do not capture the compositional properties of natural scenes. In this paper, we present the Slot Attention module, an architectural component that interfaces with perceptual representations such as the output of a convolutional neural network and produces a set of task-dependent abstract representations which we call slots. These slots are exchangeable and can bind to any object in the input by specializing through a competitive procedure over multiple rounds of attention. We empirically demonstrate that Slot Attention can extract object-centric representations that enable generalization to unseen compositions when trained on unsupervised object discovery and supervised property prediction tasks.

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An active contour model for medical image segmentation using a quaternion framework 18

An active contour model for medical image segmentation using...

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18th Image Processing: Algorithms and systems Conference, IPAS 2020

作者： Voronin, V. Zhdanova, M. Semenishchev, E. Zelensky, A. Agaian, S. Moscow State University of Technology "STANKIN" Moscow Russia Don State Technical University Lab. «Mathematical methods of image processing and computer vision Intelligent Systems» Gagarina 1 Rostov on Don Russia CUNY/ College of Staten Island Dept. of Computer Science New York United States

This paper presents a new method for segmenting medical images is based on Hamiltonian quaternions and the associative algebra, method of the active contour model and LPA-ICI (local polynomial approximation - the intersection of confidence intervals) anisotropic gradient. Since for segmentation tasks, the image is usually converted to grayscale, this leads to the loss of important information about color, saturation, and other important information associated color. To solve this problem, we use the quaternion framework to represent a color image to consider all three channels simultaneously when segmenting the RGB image. As a method of noise reduction, adaptive filtering based on local polynomial estimates using the ICI rule is used. The presented new approach allows obtaining clearer and more detailed boundaries of objects of interest. The experiments performed on real medical images (Z-line detection) show that our segmentation method of more efficient compared with the current state-of-art methods. © 2020, Society for Imaging science and Technology

关键词： Adaptive filtering

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Sequential Action-Based Dynamic Decision-Support Model for Urban Ecological Planning

SSRN

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SSRN 2023年

作者： Kim, Eun Sub Yun, Seok Hwan Kim, Ji Yeon Thorne, James H. Hyun, Jung Hee Lee, Dong Kun Interdisciplinary Program in Landscape Architecture Seoul National University Seoul08826 Korea Republic of Integrated Major in Smart City Global Convergence Program Seoul National University Seoul08826 Korea Republic of Specialized Graduate School of Intelligent Eco-Science 4 Dept. of Landscape Architecture Seoul National University Seoul08826 Korea Republic of Smart Green Solution Team SK forest Department of Environmental Science and Policy University of California One Shields Ave DavisCA95616 United States Systemic Risk and Resilience Research Group Advancing Systems Analysis International Institute for Applied Systems Analysis Austria Department of Landscape Architecture and Rural System Engineering Seoul National University Seoul08826 Korea Republic of

Decision-makers often encounter the challenge of efficient long-term ecological planning to consider economic and environmental trade-offs. However, tools to support stepwise mitigation strategies that account for dynamic ecological responses to continuing urban development are limited. Therefore, we developed a sequential action-based dynamic decision-making model to simulate stepwise mitigation measures in space over time using a multi-objective optimization algorithm. We focused on exploring the ecological corridors and habitat creation as mitigation measures through four decades to identify trade-offs and optimal plans that minimize landscape connectivity and edge density as ecological responses to urban development and minimize implementation costs per action. The results showed that adopting an initial low-investment strategy with sequential planning significantly enhanced potential ecological benefits beyond the 2000s compared with one-action planning. In contrast, the initial high-cost investment exhibited a gradual decline in the ecological benefits over time from 1995. The initial low-cost investment presented an opportunity to enhance ecological benefits by implementing early mitigation measures in locations capable of sustaining long-term ecological functions. The results suggest that sequential planning offers valuable insights into effective urban ecological planning by analyzing time-series data. This study enables step-by-step scenario testing, thereby supporting the facilitation of urban and regional sustainability planning. © 2023, The Authors. All rights reserved.

关键词： Decision making

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CARDIOCARE platform: A beyond the state of the art approach for the management of elderly multimorbid patients with breast cancer therapy induced cardiac toxicity*

CARDIOCARE platform: A beyond the state of the art approach ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kostas M. Tsiouris Grigoris Kalliatakis Ketti Mazzocco Bostjan Šeruga Kostas Marias Georgia Karanasiou Athos Antoniades Andri Papakonstantinou Constanza Conti Manolis Tsiknakis Stelios Sfakianakis Lampros Lakkas Gerasimos Filippatos Anca Bucur Dimitrios I. Fotiadis George Manikis Davide Mauri Anastasia Constantinidou Elsa Pacella Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece Computational Bio-Medicine Laboratory Institute of Computer Science Foundation for Research and Technology – Hellas Heraklion Greece Cardiology Division European Institute of Oncology Milan Italy Department of Medical Oncology Institute of Oncology Ljubljana Ljubljana Slovenia Stremble Ventures Ltd Limassol Cyprus Dept. for Breast Cancer Endocrine Tumors and Sarcoma Theme Cancer Karolinska University Hospital Sweden Istituto di Management Sanitario S.r.l Milano Italy Institute of Computer Science Foundation for Research and Technology - Hellas Heraklion Greece 2nd Dept. of Cardiology School of Medicine University of Ioannina Ioannina Greece National and Kapodistrian University of Athens Athens Greece Dept. of Cardiology School of Medicine Attikon University Hospital Athens Greece Philips Research Europe Eindhoven Netherlands Dept. of Medical Oncology University of Ioannina Ioannina Greece Bank of Cyprus Oncology Centre Nicosia Cyprus European Society of Cardiology Brussels Belgium

Breast cancer (BC) is the most common cancer in women in Europe and worldwide, with a high prevalence in middle-aged and older women. The last years, the evolution in the existing treatment approaches have contributed to improved clinical outcomes and survival rates. Nevertheless, BC therapy-related cardiotoxicity, poses a severe impact in the short- and long-term Quality of Life (QoL) and associated survival of the BC patients. This study demonstrates how the CARDIOCARE platform and the developed risk stratification models provides healthcare professionals with a valuable tool for effectively managing BC patients, preventing treatment induced cardiotoxicity and improving their QoL. This is accomplished through the integration of multi-source patient-specific data from patient-oriented mobile applications and wearable sensors, and by the employment of beyond the state-of-the-art data mining and machine learning approaches.

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Object-Centric Learning with Slot Attention

arXiv

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arXiv 2020年

作者： Locatello, Francesco Weissenborn, Dirk Unterthiner, Thomas Mahendran, Aravindh Heigold, Georg Uszkoreit, Jakob Dosovitskiy, Alexey Kipf, Thomas Google Research Brain Team Dept. of Computer Science ETH Zurich United States Max-Planck Institute for Intelligent Systems Germany

关键词： Convolutional neural networks

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Usability testing of two mini-games and one serious game to educate people bout genetics

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Studies in Health Technology and Informatics 2019年 261卷 82-87页

作者： Mainetti, Renato Oliveri, Serena Gorini, Alessandra Cutica, Ilaria Pravettoni, Gabriella Borghese, N. Alberto Applied Intelligent Systems Laboratory AISLab Computer Science Dept. Università degli Studi di Milano Milan Italy Interdisciplinary Research Center on Decision Making Processes IRIDe Dept. of Oncology and Hemato-Oncology Università degli Studi di Milano Milan Italy

The lowering costs of DNA sequencing and the diffusion of numerous Direct to Consumer Genetic Testing (DTC-GT) services have made genetic testing easily available to the general public who can buy them without any medical prescription or consultation. Nevertheless, the knowledge required to understand the provided results and their implications is still scarce in the general public. Starting from these considerations, we developed two mini-games and one serious game to increase people genetic literacy through experiential learning. The three games were tested for usability on a sample of 30 participants, 10 for each game, who were asked to report any positive or negative issue related to the games and to fulfill the Game Experience Questionnaire in order to evaluate their playing experience. Results from the three games show that players experienced moderate levels of immersion and flow, low levels of negative sensations, and a prevalence of positive emotions. In general, these encouraging results suggest that the proposed games are suitable to transmit genetic notions to the general public. © 2019 The authors and IOS Press.

关键词： Serious games

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Challenging common assumptions in the unsupervised learning of disentangled representations

Challenging common assumptions in the unsupervised learning ...

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2019 Reproducibility in Machine Learning, RML@ICLR 2019 Workshop

作者： Locatello, Francesco Bauer, Stefan Lucic, Mario Rätsch, Gunnar Gelly, Sylvain Schölkopf, Bernhard Bachem, Olivier Google AI Brain Team United States ETH Zurich Dept. for Computer Science Switzerland Max-Planck Institute for Intelligent Systems Germany

The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. In this paper, we provide a sober look on recent progress in the field and challenge some common assumptions. We train more than 12 000 models covering most prominent methods and evaluation metrics in a reproducible large-scale experimental study on seven different data sets. We observe that while the different methods successfully enforce properties "encouraged" by the corresponding losses, well-disentangled models seemingly cannot be identified without supervision. Furthermore, increased disentanglement does not seem to lead to a decreased sample complexity of learning for downstream tasks. Our results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets. © RML@ICLR 2019 Workshop - Reproducibility in Machine Learning. All Rights Reserved.

关键词： Unsupervised learning

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