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arXiv 2020年

作者： Egger, Jan Pepe, Antonio Gsaxner, Christina Jin, Yuan Li, Jianning Kern, Roman Institute of Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria University Medicine Essen Essen Germany Research Center for Connected Healthcare Big Data Zhejiang Lab Zhejiang Hangzhou China Research Unit Experimental Neurotraumatology Department of Neurosurgery Medical University of Graz Graz Austria Knowledge Discovery Know-Center Graz Austria Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image,

关键词： Surveys

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Agent-based simulations for protecting nursing homes with prevention and vaccination strategies

arXiv

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arXiv 2020年

作者： Lasser, Jana Zuber, Johannes Sorger, Johannes Dervic, Elma Ledebur, Katharina Lindner, Simon David Klager, Elisabeth Kletečka-Pulker, Maria Willschke, Harald Stangl, Katrin Stadtmann, Sarah Haslinger, Christian Klimek, Peter Wochele-Thoma, Thomas Graz University of Technology Institute for Interactive Systems and Data Science Inffeldgasse 16C Graz8010 Austria Complexity Science Hub Vienna Josefstädterstraße 39 Vienna1080 Austria Medical University Vienna Section for Science of Complex Systems Center for Medical Statistics Informatics and Intelligent Systems Spitalgasse 23 Vienna1090 Austria Research Institute of Molecular Pathology Campus-Vienna-Biocenter 1 Vienna1030 Austria Ludwig Boltzmann Institute for Digital Health and Patient Safety Spitalgasse 23 BT86 Vienna1090 Austria University Vienna Institut für Ethik und Recht in der Medizin Spitalgasse 2-4 Vienna1090 Austria Caritas Erzdiözese Wien Albrechtskreithgasse 19-21 Vienna1160 Austria Hygienefachkraft-unlimited Oswaldgasse 14-22/2/11 Vienna1120 Austria

Due to its high lethality amongst the elderly, the safety of nursing homes has been of central importance during the COVID-19 pandemic. With test procedures becoming available at scale, such as antigen or RT-LAMP tests, and increasing availability of vaccinations, nursing homes might be able to safely relax prohibitory measures while controlling the spread of infections (meaning an average of one or less secondary infections per index case). Here, we develop a detailed agent-based epidemiological model for the spread of SARS-CoV-2 in nursing homes to identify optimal prevention strategies. The model is microscopically calibrated to high-resolution data from nursing homes in Austria, including detailed social contact networks and information on past outbreaks. We find that the effectiveness of mitigation testing depends critically on the timespan between test and test result, the detection threshold of the viral load for the test to give a positive result, and the screening frequencies of residents and employees. Under realistic conditions and in absence of an effective vaccine, we find that preventive screening of employees only might be sufficient to control outbreaks in nursing homes, provided that turnover times and detection thresholds of the tests are low enough. If vaccines that are moderately effective against infection and transmission are available, control is achieved if 80% or more of the inhabitants are vaccinated, even if no preventive testing is in place and residents are allowed to have visitors. Since these results strongly depend on vaccine efficacy against infection, retention of testing infrastructures, regular voluntary screening and sequencing of virus genomes is advised to enable early identification of new variants of concern. © 2020, CC BY-NC-SA.

关键词： Hospitals

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Explainable AI (ex-AI)

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Informatik-Spektrum 2018年第2期41卷 138-143页

作者： Holzinger, Andreas Institut für Medizinische Informatik Statistik & Dokumenation Holzinger Group HCI-KDD Medizinische Universität Graz Auenbruggerplatz 2/V Graz8036 Austria Fakultät für Informatik & Biomedizinische Technik Institute for Interactive Systems and Data Science Technische Universität Graz Auenbruggerplatz 2/V Graz8036 Austria

Zusammenfassung,,Explainable AI“ ist kein neues Gebiet. Vielmehr ist das Problem der Erklärbarkeit so alt wie die AI selbst, ja vielmehr das Resultat ihrer selbst. Während regelbasierte Lösungen der frühen AI nachvollziehbare ,,Glass-Box“-Ansätze darstellten, lag deren Schwäche im Umgang mit Unsicherheiten der realen Welt. Durch die Einführung probabilistischer Modellierung und statistischer Lernmethoden wurden die Anwendungen zunehmend erfolgreicher - aber immer komplexer und opak. Beispielsweise werden Wörter natürlicher Sprache auf hochdimensionale Vektoren abgebildet und dadurch für Menschen nicht mehr verstehbar. In Zukunft werden kontextadaptive Verfahren notwendig werden, die eine Verknüpfung zwischen statistischen Lernmethoden und großen Wissensrepräsentationen (Ontologien) herstellen und Nachvollziehbarkeit, Verständlichkeit und Erklärbarkeit erlauben - dem Ziel von ,,explainable AI“.

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Treatment outcome clustering patterns correspond to discrete asthma phenotypes in children

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Asthma research and practice 2021年第1期7卷 11页

作者： Ivana Banić Mario Lovrić Gerald Cuder Roman Kern Matija Rijavec Peter Korošec Mirjana Turkalj Srebrnjak Children's Hospital Srebrnjak 100 10000 Zagreb Croatia. Know-Center Infeldgasse 13 Graz AT-8010 Austria. lovric@tugraz.at. Institute of Interactive Systems and Data Science Graz University of Technology Inffeldgasse 16C AT-8010 Graz Austria. lovric@tugraz.at. Institute of Interactive Systems and Data Science Graz University of Technology Inffeldgasse 16C AT-8010 Graz Austria. Know-Center Infeldgasse 13 Graz AT-8010 Austria. University Clinic of Respiratory and Allergic Diseases Golnik Golnik 36 4204 Golnik Slovenia. Faculty of Medicine J.J Strossmayer University of Osijek Josipa Huttlera 4 31000 Osijek Croatia. Catholic University of Croatia Ilica 242 10000 Zagreb Croatia.

Despite widely and regularly used therapy asthma in children is not fully controlled. Recognizing the complexity of asthma phenotypes and endotypes imposed the concept of precision medicine in asthma treatment. By applying machine learning algorithms assessed with respect to their accuracy in predicting treatment outcome, we have successfully identified 4 distinct clusters in a pediatric asthma cohort with specific treatment outcome patterns according to changes in lung function (FEV and MEF), airway inflammation (FENO) and disease control likely affected by discrete phenotypes at initial disease presentation, differing in the type and level of inflammation, age of onset, comorbidities, certain genetic and other physiologic traits. The smallest and the largest of the 4 clusters- 1 (N = 58) and 3 (N = 138) had better treatment outcomes compared to clusters 2 and 4 and were characterized by more prominent atopic markers and a predominant allelic (A allele) effect for rs37973 in the GLCCI1 gene previously associated with positive treatment outcomes in asthmatics. These patients also had a relatively later onset of disease (6 + yrs). Clusters 2 (N = 87) and 4 (N = 64) had poorer treatment success, but varied in the type of inflammation (predominantly neutrophilic for cluster 4 and likely mixed-type for cluster 2), comorbidities (obesity for cluster 2), level of systemic inflammation (highest hsCRP for cluster 2) and platelet count (lowest for cluster 4). The results of this study emphasize the issues in asthma management due to the overgeneralized approach to the disease, not taking into account specific disease phenotypes.

关键词： Allergy Asthma phenotypes Childhood asthma Clustering Machine learning Treatment outcome

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Analytics for everyday learning from two perspectives: Knowledge workers and teachers 1

Analytics for everyday learning from two perspectives: Knowl...

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1st Workshop on Analytics for Everyday Learning, AFEL 2018

作者： Fessl, Angela Kowald, Dominik Sola, Susana López Moreno, Ana Maturana, Ricardo Alonso Thalmann, Stefan Know-Center Inffeldgasse 13 Graz8010 Austria Institute of Interactive Systems and Data Science Graz University of Technology Austria GNOSS - Riamintelearning Lab S.L. Piqueras 3-4th Floor Logroño-LaRioja26006 Spain

Learning analytics deals with tools and methods for analyzing and detecting patterns in order to support learners while learning in formal as well as informal learning settings. In this work, we present the results of two focus groups in which the effects of a learning resource recommender system and a dashboard based on analytics for everyday learning were discussed from two perspectives: (1) knowledge workers as self-regulated everyday learners (i.e., informal learning) and (2) teachers who serve as instructors for learners (i.e., formal learning). Our findings show that the advantages of analytics for everyday learning are three-fold: (1) it can enhance the motivation to learn, (2) it can make learning easier and broadens the scope of learning, and (3) it helps to organize and to systematize everyday learning. © 2018 CEUR-WS. All rights reserved.

关键词： Recommender systems

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A new approach to visualise accessibility problems of mobile apps in source code 20

A new approach to visualise accessibility problems of mobile...

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20th International Conference on Enterprise Information systems, ICEIS 2018

作者： Feiner, Johannes Krainz, Elmar Andrews, Keith Institute of Internet Technologies and Applications FH-JOANNEUM Werk-VI-Straße 46b Kapfenberg8605 Austria IIS Kepler University Linz Altenberger Straße 69 Linz4040 Austria Institute of Interactive Systems and Data Science Graz University of Technology Inffeldgasse 16c Graz8010 Austria

ISBN: (纸本)9789897582981

A wide range of software development is moving to the direction and domain of mobile applications. Single developer or small teams create apps for smartphones. Too often, they have not the capacity or know-how to check for usability problems and do not care for accessibility. We propose a novel workflow to bring usability issues into the development process: A quick accessibility evaluation (QAC) with 15 predefined metrics allow to collect issues. These issues are further condensed into formalised (UsabML) and the issues are tagged with the location in the source code. A dashboard view (RepoVis) showing the source code from a repository allows to spot and interactively inspect code and related issues simultaneously. © 2018 by SCITEPRESS - science and Technology Publications, Lda. All rights reserved.

关键词： Application programs

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Multi-Loop Feedback Hierarchy Involving Human Workers in Manufacturing Processes

Multi-Loop Feedback Hierarchy Involving Human Workers in Man...

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Mensch und Computer 2017, MuC 2017 - Workshopband - Human and Computer 2017, MuC 2017 - Workshop Proceedings

作者： Gursch, Heimo Cemernek, David Kern, Roman Knowledge Discovery Group Know-Center GmbH Austria Institute for Interactive Systems and Data Science Graz University of Technology Austria

In manufacturing environments today, automated machinery works alongside human workers. In many cases computers and humans oversee different aspects of the same manufacturing steps, sub-processes, and processes. This paper identifies and describes four feedback loops in manufacturing and organises them in terms of their time horizon and degree of automation versus human involvement. The data flow in the feedback loops is further characterised by features commonly associated with Big data. Velocity, volume, variety, and veracity are used to establish, describe and compare differences in the data flows. Copyright (C) 2017 bei den Autoren.

关键词： Manufacture

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Graz university of technology at CL-SciSumm 2017: Query generation strategies

Graz university of technology at CL-SciSumm 2017: Query gene...

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2017 Computational Linguistics Scientific Summarization Shared Task, CL-SciSumm 2017

作者： Felber, Thomas Kern, Roman Institute of Interactive Systems and Data Science Graz University of Technology Know-Center GmbH Ineldgasse 13 Graz8010 Austria

In this report we present our contribution to the 3rd Computational Linguistics Scientific Document Summarization Shared Task (CL-SciSumm 2017), which poses the challenge of identifying the spans of text in a reference paper (RP) that most accurately reect a citation (i.e. citance) from another document to the RP. In our approach, we address this challenge by applying techniques from the field of information retrieval. Therefore we create a separate index for every RP and then transform each citance to a RP into a query. This query is subsequently used to retrieve the most relevant spans of text from the RP. Different ranking models and query generation strategies have been employed to alter which spans of text are retrieved from the index. Furthermore we implemented a k-nn classification based on our search infrastructure for assigning the cited text span to pre-defined classes.

关键词： Information retrieval

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BioIoT: Communicating sensory information of a coffee machine using a nature metaphor

BioIoT: Communicating sensory information of a coffee machin...

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2017 ACM SIGCHI Conference on Human Factors in Computing systems, CHI EA 2017

作者： Barreiros, Carla Alexandra Souta Veas, Eduardo E. Pammer, Viktoria Interactive Systems and Data Science Institute Graz University of Technology Graz Austria Know-Center GmbH Inffeldgasse 13/6 Graz Austria

ISBN: (纸本)9781450346566

In our research we explore representing the state of production machines using a new nature metaphor, called BioIoT. The underlying rationale is to represent relevant information in an agreeable manner and to increase machines' appeal to operators. In this paper we describe a study with twelve participants in which sensory information of a coffee machine is encoded in a virtual tree. All participants considered the interaction with the BioIoT pleasant;and most reported to feel more inclined to perform machine maintenance, take "care" for the machine, than given classic state representation. The study highlights as directions for follow-up research personalization, intelligibility vs representational power, limits of the metaphor, and immersive visualization. Copyright © 2017 by the Association for Computing Machinery, Inc. (ACM).

关键词： Visual languages

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