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A STUDY ON INTERIOR Γ-HYPERFILTERS IN ORDERED Γ-SEMIHYPERGROUPS

UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2022年第1期84卷 71-80页

作者： Chen, C. Kosari, S. Omidi, S. Davvaz, B. Akhoundi, M. Institute of Software School of Electronics Engineering and Computer Sciences Peking University Beijing100871 China Institute of Computing Science and Technology Guangzhou University Guangzhou510006 China Ministry of Education Iran Department of Education in Tehran Tehran Iran Department of Mathematics Yazd University Yazd Iran Clinical Research Development Unit of Rouhani Hospital Babol University of Medical Sciences Babol Iran

In this paper, interior Γ-hyperfilters of ordered Γ-semihypergroups are de- fined and various types of them are studied. Results related to productional ordered Γ- semihypergroups were investigated. Moreover, we investigate some properties of the in- verse images of strong interior Γ-hyperfilters in ordered Γ-semihypergroups. Finally, we discuss the relationship between two fundamental notions of ordered Γ-semihypergroup, the several types of interior Γ-hyperfilters and the (completely) prime interior Γ-hyperideals. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： (completely) prime I-Γ-hyperfilter Ordered Γ-semihypergroup Strong I-Γ-hyperfilter Weak I-Γ-hyperfilter

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Colour Channel Separation and Recombination of Images to Improve Object Detection of Diffuse Characters 28th

Colour Channel Separation and Recombination of Images to Imp...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Roy, Mayank Håkansson, Anne Backman, Ann-Sofie Wijkström, Camilla Varkey, Jonas Salih, Naz Mohammed Papachrysos, Nikolaos Mannheimer, Olle Schmidt, Peter Thelin Brackmann, Stephan de Lange, Thomas Department of Computer Science UiT The Arctic University of Norway Tromsø9019 Norway KTH Royal Institute of Technology Department of Software and Computer Systems KistaSE-164 40 Sweden Dept. of Medicine Huddinge Karolinska Institutet Stockholm141 86 Sweden Gastroenterology unit Dept. of Medicine Ersta Hospital Stockholm116 30 Sweden Division of Gastroenterology Department of Medicine Sahlgrenska University Hospital Gothenburg405 30 Sweden Department of Molecular and Clinical Medicine Institute of Medicine University of Gothenburg Gothenburg405 30 Sweden Sahlgrenska University Hospital/Mölndal Göteborgsvägen 31 Mölndal431 30 Sweden Sahlgrenska University Hospital Östra Diagnosvägen 11 Gothenburg416 50 Sweden Department of Medical Sciences Uppsala University Uppsala751 85 05 Sweden Department of Medicine and Emergencies Sahlgrenska University Hospital/Mölndal Västra Götalandsregionen Mölndal431 30 Sweden Department of Gastroenterology Division of Medicine Akershus University Hospital Lørenskog1478 Norway Institute for Clinical Medicine University of Oslo Oslo Norway Region Västra Götaland Sahlgrenska University Hospital Department of Research Development Education and Innovation Sahlgrenska University Hospital Gothenburg413 90 Sweden

Deep Learning has the ability to train on datasets created from videos. This facility makes deep learning algorithms suitable for detecting distinct objects in large sets of frames, particularly for delineating anomalies like precancerous lesions during surveillance colonoscopies of the large bowel. However, capturing subtle, diffuse characteristics in these videos' frames can be a challenge. This paper presents a deep learning system that uses colour channel separation and recombination of images to improve the performance of an object detection model, to tackle this challenge. Using a dataset from surveillance colonoscopy videos to find precancerous and cancerous lesions in IBD patients, individual colour channels of RGB images in the dataset are separated and recombined to form different datasets which are later used to train YOLOv8x models. The object detection model that is trained and tested with only the blue channel component of the images in the dataset performs better and gives more accurate predictions than the object detection model that is trained and tested with the datasets containing the green and/or the red channel images, as well as the dataset containing the original RGB images. © 2024 The Authors.

关键词： RGB color model

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Colour Channel Separation and Recombination of Images to Improve Object Detection of Diffuse Characters

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Procedia Computer Science 2024年 246卷 3457-3466页

作者： Mayank Roy Anne Håkansson Ann-Sofie Backman Camilla Wijkström Jonas Varkey Naz Mohammed Salih Nikolaos Papachrysos Olle Mannheimer Peter Thelin Schmidt Stephan Brackmann Thomas de Lange Department of Computer Science UiT The Arctic University of Norway 9019 Tromsø Norway KTH Royal Institute of Technology Department of Software and Computer Systems SE-164 40 Kista Sweden Dept. of Medicine Huddinge Karolinska Institutet 141 86 Stockholm Sweden Gastroenterology unit Dept. of Medicine Ersta Hospital 116 30 Stockholm Sweden Division of Gastroenterology Department of Medicine Sahlgrenska University Hospital 405 30 Gothenburg Sweden Department of Molecular and Clinical Medicine Institute of Medicine University of Gothenburg 405 30 Gothenburg Sweden Sahlgrenska University Hospital/Mölndal Göteborgsvägen 31 431 30 Mölndal Sweden Sahlgrenska University Hospital/Östra Diagnosvägen 11 416 50 Gothenburg Sweden Region Västra Götaland Sahlgrenska University Hospital Department of Research Development Education and Innovation Sahlgrenska University Hospital 413 90 Gothenburg Sweden Department of Medical Sciences Uppsala University 751 85 05 Uppsala Sweden Department of Gastroenterology Division of Medicine Akershus University Hospital 1478 Lørenskog Norway Institute for Clinical Medicine University of Oslo Oslo Norway Department of Medicine and Emergencies Sahlgrenska University Hospital/Mölndal Västra Götalandsregionen 431 30 Mölndal Sweden

Deep Learning has the ability to train on datasets created from videos. This facility makes deep learning algorithms suitable for detecting distinct objects in large sets of frames, particularly for delineating anomalies like precancerous lesions during surveillance colonoscopies of the large bowel. However, capturing subtle, diffuse characteristics in these videos’ frames can be a challenge. This paper presents a deep learning system that uses colour channel separation and recombination of images to improve the performance of an object detection model, to tackle this challenge. Using a dataset from surveillance colonoscopy videos to find precancerous and cancerous lesions in IBD patients, individual colour channels of RGB images in the dataset are separated and recombined to form different datasets which are later used to train YOLOv8x models. The object detection model that is trained and tested with only the blue channel component of the images in the dataset performs better and gives more accurate predictions than the object detection model that is trained and tested with the datasets containing the green and/or the red channel images, as well as the dataset containing the original RGB images.

关键词： Deep Learning Object Detection Colorectal Cancer Precancerous Lesions Colour Channel Separation Recombination YOLO

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Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016

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Human genomics 2016年第1期10 Suppl 1卷 12页

作者： A. K. Srivastava Y. Wang R. Huang C. Skinner T. Thompson L. Pollard T. Wood F. Luo R. Stevenson R. Polimanti J. Gelernter X. Lin I. Y. Lim Y. Wu A. L. Teh L. Chen I. M. Aris S. E. Soh M. T. Tint J. L. MacIsaac F. Yap K. Kwek S. M. Saw M. S. Kobor M. J. Meaney K. M. Godfrey Y. S. Chong J. D. Holbrook Y. S. Lee P. D. Gluckman N. Karnani A. Kapoor D. Lee A. Chakravarti C. Maercker F. Graf M. Boutros G. Stamoulis F. Santoni P. Makrythanasis A. Letourneau M. Guipponi N. Panousis M. Garieri P. Ribaux E. Falconnet C. Borel S. E. Antonarakis S. Kumar J. Curran J. Blangero S. Chatterjee J. Akiyama D. Auer C. Berrios L. Pennacchio T. R. Donti G. Cappuccio M. Miller P. Atwal A. Kennedy A. Cardon C. Bacino L. Emrick J. Hertecant F. Baumer B. Porter M. Bainbridge P. Bonnen B. Graham R. Sutton Q. Sun S. Elsea Z. Hu P. Wang Y. Zhu J. Zhao M. Xiong David A. Bennett A. Hidalgo-Miranda S. Romero-Cordoba S. Rodriguez-Cuevas R. Rebollar-Vega E. Tagliabue M. Iorio E. D’Ippolito S. Baroni B. Kaczkowski Y. Tanaka H. Kawaji A. Sandelin R. Andersson M. Itoh T. Lassmann Y. Hayashizaki P. Carninci A. R. R. Forrest C. A. Semple E. A. Rosenthal B. Shirts L. Amendola C. Gallego M. Horike-Pyne A. Burt P. Robertson P. Beyers C. Nefcy D. Veenstra F. Hisama R. Bennett M. Dorschner D. Nickerson J. Smith K. Patterson D. Crosslin R. Nassir N. Zubair T. Harrison U. Peters G. Jarvik F. Menghi K. Inaki X. Woo P. Kumar K. Grzeda A. Malhotra H. Kim D. Ucar P. Shreckengast K. Karuturi J. Keck J. Chuang E. T. Liu B. Ji A. Tyler G. Ananda G. Carter H. Nikbakht M. Montagne M. Zeinieh A. Harutyunyan M. Mcconechy N. Jabado P. Lavigne J. Majewski J. B. Goldstein M. Overman G. Varadhachary R. Shroff R. Wolff M. Javle A. Futreal D. Fogelman L. Bravo W. Fajardo H. Gomez C. Castaneda C. Rolfo J. A. Pinto K. C. Akdemir L. Chin S. Patterson C. Statz S. Mockus S. N. Nikolaev X. I. Bonilla L. Parmentier B. King F. Bezrukov G. Kaya V. Zoete V. Seplyarskiy H. Sharpe T. McKee K. Popadin N. Basset-Seguin R. Ben Chaabene M. Andrianova C. Verdan K. Grosdemange O. Sumara M. E JCSRI Greenwood Genetic Center Greenwood USA School of Computing Clemson University Clemson USA Biochemical Genetics Laboratory Greenwood Genetic Center Greenwood USA Department Psychiatry Yale Sch Med and VA CT Healthcare Center West Haven USA Department Genetics Yale Sch Med and VA CT Healthcare Center West Haven USA Department Neurobiology Yale Sch Med and VA CT Healthcare Center West Haven USA Singapore Institute for Clinical Sciences Singapore Singapore National University of Singapore Singapore Singapore University of British Columbia Vancouver Canada KK Women’s and Children’s Hospital Singapore Singapore University of Southampton and University Hospital Southampton NHS Foundation Trust Southampton UK University of Auckland Auckland New Zealand McKusick-Nathans Institute of Genetic Medicine Johns Hopkins University School of Medicine Baltimore USA Esslingen University of Applied Sciences Esslingen Germany German Cancer Research Center Heidelberg Germany Department of Genetic Medicine and Development University of Geneva Medical School Geneva Switzerland Geneva University Hospitals-HUG Service of Genetic Medicine Geneva Switzerland GE3 Institute of Genetics and Genomics of Geneva University of Geneva Medical School Geneva Switzerland South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Edinburg USA South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Brownsville USA Institute of Genetic Medicine Johns Hopkins University Baltimore USA Genomics Division Lawrence Berkeley National Laboratory Berkeley USA Molecular and Human Genetics Baylor College of Medicine Houston USA Department of Translational Medical Sciences Federico II University Naples Italy Metabolon Inc Durham USA Section of Pediatric Neurology and Neuroscience Baylor College of Medicine Houston USA Tawam Hospital Abu Dhabi United Arab Emirates Stanford Medical School Stanford USA Sch

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus A. Kapoor, D. Lee, A. Chakravarti O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells C. Maercker, F. Graf, M. Boutros O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis O7 Role of microRNA in LCL to IPSC reprogramming S. Kumar, J. Curran, J. Blangero O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti O9 Metabolomic profiling for the diagnosis of neurometabolic disorders T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea O10 A novel causal methylation network approach to Alzheimer’s

关键词： Pulmonary Arterial Hypertension Oral Squamous Cell Carcinoma Deep Neural Network Autosomal Recessive Polycystic Kidney Disease Whole Genome Bisulfite Sequencing

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BLOOD DONORS AND BLOOD COLLECTION: Web interface–supported transmission risk assessment and cost-effectiveness analysis of postdonation screening: a global model applied to Ghana, Thailand, and the Netherlands

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Transfusion 2009年第12期49卷

作者： Marinus Van Hulst Gijs A.A. Hubben Kwamena W.C. Sagoe Charupon Promwong Parichart Permpikul Ladda Fongsatitkul Diarmuid M. Glynn Cees T. Smit Sibinga Maarten J. Postma From the Unit of PharmacoEpidemiology and PharmacoEconomics (PE2) Department of Pharmacy University of Groningen the Department of Clinical Pharmacy and Toxicology Martini Hospital and Health Economics Consultancy and Technology Assessments (HECTA) Groningen The Netherlands BaseCase GmbH Decision Support Software Zug Switzerland the Department of Microbiology University of Ghana Medical School Accra Ghana the Blood Bank and Transfusion Medicine Unit Faculty of Medicine Prince of Songkla University Songkla Thailand the Department of Transfusion Medicine Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand the Blood Bank Unit Faculty of Medicine Chiang Mai University Chiang Mai Thailand and the Academic Institute for International Development of Transfusion Medicine Sanquin Consulting Services and WHO Collaborating Centre for Blood Transfusion Groningen The Netherlands.

BACKGROUND: The goal of our research was to actively involve decision makers in the economic assessment of screening strategies in their region. This study attempted to accomplish this by providing an easy-to-use Web interface at http://*** that allows decision makers to adapt this model to local conditions. STUDY DESIGN AND METHODS: The cost-effectiveness was compared of 1) adding antigen screening to antibody screening for hepatitis C virus (HCV) and human immunodeficiency virus (HIV); 2) adding nucleic acid amplification testing (NAT) on hepatitis B virus (HBV), HCV, and HIV in minipool (pool of 6 [MP6] and 24 [MP24]) to antibody screening and hepatitis B surface antigen (HBsAg) screening; and 3) individual-donation NAT on HBV, HCV, and HIV to antibody screening and HBsAg screening for Ghana, Thailand, and the Netherlands. RESULTS: The combination of HCV antibody-antigen combination (combo) and HIV combo added to antibody screening in Ghana and Thailand was cost-effective according to the WHO criteria. MP24-NAT screening in Ghana was also cost-effective. MP24-NAT on HBV, HCV, and HIV was not cost-effective compared to the other screening strategies evaluated for the Netherlands. Large regional differences in cost-effectiveness were found for Thailand. CONCLUSION: The young transfusion recipient population of Ghana in combination with a high risk of viral transmission yields better cost-effectiveness for additional tests. The advanced age of the transfused population of the Netherlands and a small risk of viral transmission gives poor cost-effectiveness for more sensitive screening techniques. It was demonstrated that a global health economic model combined with a Web interface can provide easy access to risk assessment and cost-effectiveness analysis.

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H-Life: An intelligent personal health assistant

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Health Informatics Journal 2002年第1期8卷 20-28页

作者： Nanou, D.G. Poulis, A.M. Likas, A. Fotiadis, D.I. Medical Technology and Software Development Unit Department of Computer Science University of Ioannina Ioannina Biomedical Research Institute 45110 Ioannina Greece Department of Computer Science University of Ioannina Ioannina Biomedical Research Institute 45110 Ioannina Greece

In this paper the architecture and subsystems of the h-Life platform are described. The system is currently under implementation and therefore all system components and modules are described according to their design specifications. h-Life aims at developing a user-friendly, personalized and ubiquitously accessible knowledge-based system that will provide citizens with an intelligent tool for monitoring the state of health and lifestyle. The h-Life system is based on the integration of several components: • a knowledge repository related to health, nutrition, fitness and lifestyle• a user repository where personal health records and user profiles are stored• a plan repository for the storage of medical plans• an intelligent adviser and plan selection mechanism, which provides advice and plans based on the user profile• an alerting and reminding module that issues alerts (to ensure the timely execution of a selected plan) and notifications concerning events of potential interest• a customer-facing module, which ensures personalized interaction with the system and customizes the user-system interaction according to the user profile. Based on the latest technology in intelligent and communication systems, the h-Life environment aims at providing the user with personalized, ‘certified’ lifestyle information and consultancy, tailored to individual needs and available anywhere and at anytime. © 2002, Sage Publications. All rights reserved.

关键词： Intelligent systems knowledge-based systems mobile access personal health records personalization

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Epirus-net: a wireless health telematics network in Greece

Epirus-net: a wireless health telematics network in Greece

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

作者： N.A. Goulas D.G. Nanou D.I. Fotiadis Department of Computer Science Medical Technology and Software Development Unit University of Ioannina (UoI) Ioannina Greece

The Region of Epirus, located in a rather isolated and up to now underdeveloped part of Greece, has made a significant effort in establishing and promoting an integrated environment for health care delivery through the development of a regional Health Telematics Network, the Epirus-Net. Epirus-Net is a fixed wireless data network, that gradually extends throughout the region and connects regional hospitals and health care centers. The network provides access to Integrated Electronic Health Record services, resource services (Directory of Healthcare Providers), educational services (distant learning courses on medicine) as well as specialized medical tele-conference and tele-consultation services. Within the context of this paper the network infrastructure and the computer-based patient record, CPR, system, is described.

关键词： Telematics Intelligent networks medical services Hospitals Spine Teleconferencing Telemedicine Communications technology Asynchronous transfer mode Switches

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XMedIA communication system: an XML driven medical image processing and archiving environment

XMedIA communication system: an XML driven medical image pro...

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

作者： G.C. Sakellaris D.I. Fotiadis Department of Computer Science Medical Technology and Software Development Unit University of Ioannina (UoI) Ioannina Greece

This paper addresses the contribution of XML and related technologies, to the integration of a distributed healthcare system that inherits characteristics from the extended framework of eXtended Markup Language, XML, the distributed Simple Object Access Protocol, SOAP and Distributed Component Object Model, DCOM, architecture. The solution that this system suggests is a two-module architecture that involves the familiar, secure and effective environment of a distributed architecture using DCOM and the flexible, lightweight and ideal for distributed applications protocol named SOAP. The XMedIA system meets the requirements for secure search, transaction, and presentation of medical information to the end users, whether patient or healthcare professional. Key features of the work include personalized access and efficient management of distributed medical knowledge, intercommunication of heterogeneous systems and presentation of information in a concise way, across different platforms.

关键词： XML Biomedical image processing Biomedical imaging medical services Simple object access protocol Personal digital assistants Markup languages Object oriented modeling Distributed databases Image databases

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