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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 1991年 498 LNCS卷 3-3页

作者： Andersen, Rudolf Bubenko, Janis A. Sølvberg, Arne Information and Knowledge Systems Group Department of Electrical Engineering and Computer Science The Norwegian Institute of Technology TrondheimN-7034 Norway SISU-Swedish Institute for Systems Development Box 1250 KistaS-164 28 Sweden

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Sure: Screening Unlabeled Samples for Reliable Negative Samples Based on Reinforcement Learning

SSRN

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SSRN 2022年

作者： Li, Ying Sun, Hang Fang, Wensi Ma, Qin Han, Siyu Wang-Sattler, Rui Du, Wei Yu, Qiong Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Qianjin Street Jilin Changchun130012 China Department of Biomedical Informatics Ohio State University Lane Ave ColumbusOH43210 United States TUM School of Medicine Technical University of Munich Ismaninger Straße 22 Bavaria Munich81675 Germany Helmholtz Center Munich German Research Center for Environmental Health Ingolstadter Landstraße 1 Bavaria Neuherberg85764 Germany Department of Epidemiology and Biostatistics School of public health Jilin University Qianjin Street Jilin Changchun130012 China

For many classification tasks, only experimentally validated positive samples are available, and experimentally validated negative samples are not recorded. The lack of negative samples poses a great challenge for using supervised machine learning. To address this problem, we propose a novel deep reinforcement learning based model to screen reliable negative samples from unlabeled samples, named SURE. SURE has two modules: sample selector and sample inspector. The sample selector screens reliable negative samples from unlabeled samples by two reinforcement strategies. The sample inspector classifies samples and provides rewards to the sample selector. In this paper, we focus on one popular issue in the field of bioinformatics: the ncRNA-protein interaction (NPI) prediction task, which lacks reliable negative samples. Thirty datasets for NPI prediction are used to test the screening effect of SURE. The Experimental results show that our model has a robust negative sample screening capability and is superior to all outstanding sample screening methods used in the NPI prediction task. In addition, we refine 5 NPI datasets containing reliable negative samples screened by SURE, and a webserver (***/sure) is available offering the NPI prediction refined by SURE. © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Web-Based Platform for People with Memory Problems and Their Caregivers (CAREGIVERSPRO-MMD):Mixed-Methods Evaluation of Usability

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JMIR Formative Research 2018年第1期2卷 e4-e4页

作者： Zafeiridi, Paraskevi Paulson, Kevin Dunn, Rosie Wolverson, Emma White, Caroline Thorpe, Jonathan Adrian Antomarini, Marco Cesaroni, Francesca Scocchera, Francesca Landrin-Dutot, Isabelle Malherbe, Laetitia Lingiah, Hendi Berard, Marie Girones, Xavier Quintana, Maria Cortes, Ulises Barrue, Cristian Cortes, Atia Paliokas, Ioannis Votis, Konstantinos Tzovaras, Dimitrios School of Engineering and Computer Science University of Hull School of Health and Social Work Aire Building Cottingham Road Hull HU67RX United Kingdom Cooperativa Sociale Onlus Marche Onlus Ancona Italy Department of Internal Medicine Geriatrics and Therapeutics Rouen University Hospital Department of Geriatrics Rouen Cedex France Faculty of Health Sciences University of Vic Central University of Catalonia Manresa Spain Knowledge Engineering & Machine Learning Group Universitat Polit cnica de Catalunya Barcelona Spain Information Technologies Institute Centre for Research and Technology Hellas Thessaloniki Greece

Background: The increasing number of people with dementia (PwD) drives research exploring Web-based support interventions to provide effective care for larger populations. In this concept, a Web-based platform (CAREGIVERSPRO-MMD, 620911) was designed to (1) improve the quality of life for PwD, (2) reduce caregiver burden, (3) reduce the financial costs for care, and (4) reduce administration time for health and social care professionals. Objective: The objective of this study was to evaluate the usability and usefulness of CAREGIVERSPRO-MMD platform for PwD or mild cognitive impairment (MCI), informal caregivers, and health and social care professionals with respect to a wider strategy followed by the project to enhance the user-centered approach. A secondary aim of the study was to collect recommendations to improve the platform before the future pilot study. Methods: A mixed methods design was employed for recruiting PwD or MCI (N=24), informal caregivers (N=24), and professionals (N=10). Participants were asked to rate their satisfaction, the perceived usefulness, and ease of use of each function of the platform. Qualitative questions about the improvement of the platform were asked when participants provided low scores for a function. Testing occurred at baseline and 1 week after participants used the platform. The dropout rate from baseline to the follow-up was approximately 10% (6/58). Results: After 1 week of platform use, the system was useful for 90% (20.75/23) of the caregivers and for 89% (5.36/6) of the professionals. When users responded tomore than 1 question per platform function, the mean of satisfied users per function was calculated. These user groups also provided positive evaluations for the ease of use (caregivers: 82%, 18.75/23;professionals: 97%, 5.82/6) and their satisfaction with the platform (caregivers: 79%, 18.08/23;professionals: 73%, 4.36/6). Ratings from PwD were lower than the other groups for usefulness (57%, 13/23), ease of use (41%

关键词： Caregivers Dementia Social support Technology

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Coastal water quality prediction based on machine learning with feature interpretation and Spatio-temporal analysis

arXiv

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arXiv 2021年

作者： Grbčić, Luka Družeta, Siniša Mauša, Goran Lipić, Tomislav Lušić, Darija Vukić Alvir, Marta Lučin, Ivana Sikirica, Ante Davidović, Davor Travaš, Vanja Kalafatović, Daniela Pikelj, Kristina Fajković, Hana Holjević, Toni Kranjčević, Lado Department of Fluid Mechanics and Computational Engineering Faculty of Engineering University of Rijeka Vukovarska 58 Rijeka51000 Croatia Department of Computer Engineering Faculty of Engineering University of Rijeka Vukovarska 58 Rijeka51000 Croatia Center for Advanced Computing and Modelling University of Rijeka Radmile Matejčić 2 Rijeka51000 Croatia Department of Hydrotechnics and Geotechnics Faculty of Civil Engineering University of Rijeka Radmile Matejčić 3 Rijeka51000 Croatia Department of Environmental Health Faculty of Medicine University of Rijeka Braće Branchetta 20/1 Rijeka51000 Croatia Department of Environmental Health Teaching Institute of Public Health of Primorje-Gorski Kotar County Krešimirova 52a Rijeka51000 Croatia Department of Biotechnology University of Rijeka Radmile Matejčić 2 Rijeka51000 Croatia Laboratory for Machine Learning and Knowledge Representations Rudjer Boskovic Institute Biljenička cesta 54 Zagreb10000 Croatia Center for Informatics and Computing Ruđer Bošković Institute Biljenička cesta 54 Zagreb10000 Croatia Department of Geology Faculty of Science University of Zagreb Horvatovac 102a Zagreb10000 Croatia

Coastal water quality management is a public health concern, as poor coastal water quality can potentially harbor pathogens that are dangerous to human health. Tourism-oriented countries need to actively monitor the condition of coastal water at tourist popular sites during the summer season. In this study, routine monitoring data of Escherichia Coli and enterococci across 15 public beaches in the city of Rijeka, Croatia, were used to build machine learning models for predicting their levels based on environmental parameters as well as to investigate their dynamics and relationships with environmental stressors. Gradient Boosting algorithms (Catboost, Xgboost), Random Forests, Support Vector Regression and Artificial Neural Networks were trained with routine monitoring measurements from all sampling sites and used to predict E. Coli and enterococci values based on environmental features. The evaluation of stability and generalizability with 10-fold cross validation analysis of the machine learning models, showed that the Catboost algorithm performed best with R2 values of 0.71 and 0.68 for predicting E. Coli and enterococci, respectively, compared to other evaluated ML algorithms including Xgboost, Random Forests, Support Vector Regression and Artificial Neural Networks. We also use the SHapley Additive exPlanations technique to identify and interpret which features have the most predictive power. The results show that site salinity measured is the most important feature for forecasting both E. Coli and enterococci levels. Finally, the spatial and temporal predictive accuracy of both ML models were examined at sites with the historically lowest coastal water quality. The spatial *** and enterococci models achieved strong R2 values of 0.85 and 0.83, while the temporal models achieved R2 values of 0.74 and 0.67. The temporal model also achieved moderate R2 values of 0.44 and 0.46 at a site with historically high coastal water quality. © 2021, CC BY-NC-ND.

关键词： Machine learning

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The challenge of chronodisruption assessment. The case of nursing staff shift workers

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Sleep Medicine 2013年 14卷 e192-e193页

作者： Martínez Madrid, M. Campos, M. Madrid Pérez, J. Rol, M. Moreno-Casbas, T. University of Murcia Department of Physiology Faculty of Biology Spain Faculty of Computer Science Artificial Intelligence and Knowledge Engineering Spain Unidad de Investigación en Cuidados de Salud Instituto de Salud Carlos III Spain

Introduction Shift work circadian disorders have been mainly evaluated by actigraphy (A) and classical methods of time series analysis. However, to determine the degree of chronodisruption (CD) it is necessary: a) to establish a marker of internal desynchronization (including different variables in ambulatory circadian monitoring or ACM such as skin temperature WT, body position P and light exposure L) and b) to modify classical procedures, based in a unique mean waveform analysis, to take account of the day to day differences in rhythms in response to scheduled work. The aim of this work is to determine the usefulness of ACM in conjunction with a new mathematical procedure to determine the severity of CD in nursing staff. Materials and methods Ten healthy day shift and ten night shift workers (6 males and 18 females, from 22 to 62 y. o.) participated in this study. Volunteers were monitored by ACM during 7–10 days using ACM (KronowiseTM, Chronolab, Univ. of Murcia) integrating five sensors: three built into a wristwatch (T, L; environmental temperature, ET) and two on a bracelet (A and P). Circadian status was inferred using the integrated variable TAP, calculated from T, A and P. Circadian robustness was assessed by the circadian function index (CFI) of TAP variable. Internal desynchronization was measured as difference between the non-parametric phase markers of T (M5 = five consecutive hours of maximum T) and A (L5 = five consecutive hours of minimum activity). The restfulness was calculated from L5 of TAP and expressed as a score from 0 to 10 (10 = very restfully sleep). Results Despite the stability in their working schedule, permanent nocturnal shift workers showed more CD than diurnals and lower circadian robustness, higher internal desynchronization between T and A rhythms and low values in the restfulness score. When a day-to-day time series analysis was performed instead of using the mean waveform, a significant improvement in the three CD markers was obs

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CHAOS Challenge - Combined (CT-MR) healthy abdominal organ segmentation

arXiv

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arXiv 2020年

作者： Kavur, A. Emre Gezer, N. Sinem Barış, Mustafa Conze, Pierre-Henri Groza, Vladimir Pham, Duc Duy Chatterjee, Soumick Ernst, Philipp Özkan, Savaş Baydar, Bora Lachinov, Dmitry Han, Shuo Pauli, Josef Isensee, Fabian Perkonigg, Matthias Sathish, Rachana Rajan, Ronnie Aslan, Sinem Sheet, Debdoot Dovletov, Gurbandurdy Speck, Oliver Nürnberger, Andreas Maier-Hein, Klaus H. Akar, Gözde Bozdagı Ünal, Gözde Dicle, Oguz Selver, M. Alper Graduate School of Natural and Applied Sciences Dokuz Eylul University Izmir Turkey Department of Radiology Faculty Of Medicine Dokuz Eylul University Izmir Turkey Dept. of Ophthalmology and Optometry Medical Uni. of Vienna Austria Intelligent Systems Faculty of Engineering University of Duisburg-Essen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany CIR Lab Dept of Biomedical Imaging and Image-guided Therapy Medical Uni. of Vienna Austria IMT Atlantique LaTIM UMR 1101 Brest France Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Department of Electrical Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India Ca’ Foscari University of Venice ECLT DAIS Venice Italy Ege University International Computer Institute Izmir Turkey Johns Hopkins University Baltimore United States Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Magnetic Resonance Otto von Guericke University Magdeburg Germany Median Technologies Valbonne France Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Dokuz Eylul University Izmir Turkey

—Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance is hard to interpret. This makes comparative analysis a necessary tool to achieve explainable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal tasks have been rarely discussed. In order to expand the knowledge in these topics, CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge has been organized in the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Despite a large number of the previous abdomen related challenges, the majority of which are focused on tumor/lesion detection and/or classification with a single modality, CHAOS providesboth abdominal CT and MR data from healthy subjects. Five different and complementary tasks have been designed to analyze the capabilities of the current approaches from multiple perspectives. The results are investigated thoroughly, compared with manual annotations and interactive methods. The outcomes are reported in detail to reflect the latest advancements in the field. CHAOS challenge and data will be available online to provide a continuous benchmark resource for segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Medical imaging

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An Enhanced Gain of Yagi-Uda Antenna with Folded Dipole for Amateur Radio VHF Band Application

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Journal of Physics: Conference Series 2019年第1期1175卷

作者： R. E. Musril H. A. Rahim M. Abdulmalek M. Jusoh M. E. M. Salleh Bioelectromagnetics Research Group (BioEM) School of Computer and Communication Engineering Universiti Malaysia Perlis (UniMAP) Kampus Pauh Putra Perlis 02600 Arau Perlis Malaysia Department of Engineering and Information Science University of Wollongong in Dubai Block 15 Dubai Knowledge Village Dubai UAE Department of Environment Johor Malaysia

This paper presents an enhanced gain of Yagi-Uda antenna with folded dipole for amateur radio Very High Frequency (VHF) band. The analysis is performed using CST Microwave Studio Suite software. The structure of this directional antenna integrates three main parts which are directors, reflectors and feeder point of 50 Ω. The performance of the proposed antenna has a tolerable reflection coefficient below -10 dB at 144.46 MHz. A maximum gain of around 14.03 dB with a good efficiency of 75.6% is achieved. The gain of the antenna increases up to 2% when added with 7 elements of reflector than with one element of reflector and enhanced significantly by 23.6% compared to the conventional Yagi-Uda antenna.

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SMILE-UHURA Challenge - Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms

arXiv

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arXiv 2024年

作者： Chatterjee, Soumick Mattern, Hendrik Dörner, Marc Sciarra, Alessandro Dubost, Florian Schnurre, Hannes Khatun, Rupali Yu, Chun-Chih Hsieh, Tsung-Lin Tsai, Yi-Shan Fang, Yi-Zeng Yang, Yung-Ching Huang, Juinn-Dar Xu, Marshall Liu, Siyu Ribeiro, Fernanda L. Bollmann, Saskia Chintalapati, Karthikesh Varma Radhakrishna, Chethan Mysuru Ram Kumar, Sri Chandana Hudukula Sutrave, Raviteja Qayyum, Abdul Mazher, Moona Razzak, Imran Rodero, Cristobal Niederer, Steven Lin, Fengming Xia, Yan Wang, Jiacheng Qiu, Riyu Wang, Liansheng Panah, Arya Yazdan Jurdi, Rosana El Fu, Guanghui Arslan, Janan Vaillant, Ghislain Valabregue, Romain Dormont, Didier Stankoff, Bruno Colliot, Olivier Vargas, Luisa Chacón, Isai Daniel Pitsiorlas, Ioannis Arbeláez, Pablo Zuluaga, Maria A. Schreiber, Stefanie Speck, Oliver Nürnberger, Andreas Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Magdeburg Germany Department of Neurology Medical Faculty University Hospital of Magdeburg Magdeburg Germany German Centre for Neurodegenerative Diseases Magdeburg Germany Centre for Behavioural Brain Sciences Magdeburg Germany University Hospital Zurich University of Zurich Zurich Switzerland Google Inc. United States Translational Radiobiology Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexnder-Universität Erlangen-Nürnberg Erlangen Germany National Yang Ming Chiao Tung University Hsinchu Taiwan School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia Australian eHealth Research Centre Chile National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Computer Science and Engineering University of New South Wales Sydney Australia Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates The Alan Turing Institute London United Kingdom School of Computing University of Leeds Leeds United Kingdom Department of Computer Science School of Informatics Xiamen University Xiamen China Manteia Technologies Co. Ltd Xiamen China Leicester International Institute Dalian University of Technology Dalian China Sorbonne Université Institut du Cerveau - Paris Brain Institute ICM CNRS Inria Inserm AP-HP Hôpital de la Pitié Salpêtrière ParisF-75013 France Centre of Formation and Research in Artificial Intelligence Universidad de Los Andes Colombia Data Science Department EURECOM Sophia Antipolis Franc

The human brain receives nutrients and oxygen through an intricate network of blood vessels. Pathology affecting small vessels, at the mesoscopic scale, represents a critical vulnerability within the cerebral blood supply and can lead to severe conditions, such as Cerebral Small Vessel Diseases. The advent of 7 Tesla MRI systems has enabled the acquisition of higher spatial resolution images, making it possible to visualise such vessels in the brain. However, the lack of publicly available annotated datasets has impeded the development of robust, machine learning-driven segmentation algorithms. To address the complexities of mesoscopic vessel segmentation and to highlight the need for advanced techniques to manage the high noise levels and poor vessel-to-background contrast inherent in"ultra-high-resolution" data, the SMILE-UHURA challenge was organised. This challenge, held in conjunction with the ISBI 2023, in Cartagena de Indias, Colombia, aimed to provide a platform for researchers working on related topics. The SMILE-UHURA challenge addresses the gap in publicly available annotated datasets by providing an annotated dataset of Time-of-Flight angiography acquired with 7T MRI. This dataset was created through a combination of automated pre-segmentation and extensive manual refinement. In this manuscript, sixteen submitted methods and two baseline methods are compared both quantitatively and qualitatively on two different datasets: held-out test MRAs from the same dataset as the training data (with labels kept secret) and a separate 7T ToF MRA dataset where both input volumes and labels are kept secret. The results demonstrate that most of the submitted deep learning methods, trained on the provided training dataset, achieved reliable segmentation performance. Dice scores reached up to 0.838 ± 0.066 and 0.716 ± 0.125 on the respective datasets, with an average performance of up to 0.804 ± 0.15. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Correction to: MRI enhancement based on visual-attention by adaptive contrast adjustment and image fusion

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Multimedia Tools and Applications 2021年第9期80卷 13019-13019页

作者： Rui Zhu Xiongfei Li Xiaoli Zhang Xiaowei Xu Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Department of Information Sciences University of Arkansas at Little Rock Little Rock USA

A Correction to this paper has been published: https://***/10.1007/s11042-021-10782-7

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MRI-based surgical planning for lumbar spinal stenosis

arXiv

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arXiv 2017年

作者： Abbati, Gabriele Schüffler, Peter J. Winklhofer, Sebastian Burgstaller, Jakob M. Held, Ulrike Steurer, Johann Bauer, Stefan Buhmann, Joachim M. Department of Engineering Science University of Oxford Eagle House Walton Well Road OxfordOX2 6ED United Kingdom Thomas Fuchs Lab Medical Machine Learning & Computational Pathology Memorial Sloan Kettering Cancer Center 417 E 68th St. New YorkNY10065 United States Klinik für Neuroradiologie University Hospital Zürich Rämistrasse 100 Zürich8091 Switzerland Horten Centre for Patient Oriented Research Knowledge Transfer University Hospital Zürich Pestalozzistrasse 24 Zürich8032 Switzerland Department of Computer Science ETH Zürich Universittstrasse 6 Zürich8006 Switzerland

The most common reason for spinal surgery in elderly patients is lumbar spinal stenosis (LSS). For LSS, treatment decisions based on clinical and radiological information as well as personal experience of the surgeon shows large variance. Thus a standardized support system is of high value for a more objective and reproducible decision. In this work, we develop an automated algorithm to localize the stenosis causing the symptoms of the patient in magnetic resonance imaging (MRI). With 22 MRI features of each of five spinal levels of 321 patients, we show it is possible to predict the location of lesion triggering the symptoms. To support this hypothesis, we conduct an automated analysis of labeled and unlabeled MRI scans extracted from 788 patients. We confirm quantitatively the importance of radiological information and provide an algorithmic pipeline for working with raw MRI scans. Copyright © 2017, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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