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arXiv 2024年

作者： Ferber, Dyke El Nahhas, Omar S.M. Wölflein, Georg Wiest, Isabella C. Clusmann, Jan Leßman, Marie-Elisabeth Foersch, Sebastian Lammert, Jacqueline Tschochohei, Maximilian Jäger, Dirk Salto-Tellez, Manuel Schultz, Nikolaus Truhn, Daniel Kather, Jakob Nikolas Heidelberg University Hospital Heidelberg Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany School of Computer Science University of St Andrews St Andrews United Kingdom Department of Medicine II Medical Faculty Mannheim Heidelberg University Mannheim Germany Department of Medicine III University Hospital RWTH Aachen Aachen Germany Department of Medicine I University Hospital Dresden Dresden Germany Institute of Pathology University Medical Center Mainz Mainz Germany Munich Germany Munich Germany partner site Munich Germany Initiative partner site Munich Germany Google Cloud Munich Germany Integrated Pathology Unit Institute for Cancer Research Royal Marsden Hospital London United Kingdom Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Department of Diagnostic and Interventional Radiology University Hospital Aachen Germany

Multimodal artificial intelligence (AI) systems have the potential to enhance clinical decision-making by interpreting various types of medical data. However, the effectiveness of these models across all medical fields is uncertain. Each discipline presents unique challenges that need to be addressed for optimal performance. This complexity is further increased when attempting to integrate different fields into a single model. Here, we introduce an alternative approach to multimodal medical AI that utilizes the generalist capabilities of a large language model (LLM) as a central reasoning engine. This engine autonomously coordinates and deploys a set of specialized medical AI tools. These tools include text, radiology and histopathology image interpretation, genomic data processing, web searches, and document retrieval from medical guidelines. We validate our system across a series of clinical oncology scenarios that closely resemble typical patient care workflows. We show that the system has a high capability in employing appropriate tools (97%), drawing correct conclusions (93.6%), and providing complete (94%), and helpful (89.2%) recommendations for individual patient cases while consistently referencing relevant literature (82.5%) upon instruction. This work provides evidence that LLMs can effectively plan and execute domain-specific models to retrieve or synthesize new information when used as autonomous agents. This enables them to function as specialist, patient-tailored clinical assistants. It also simplifies regulatory compliance by allowing each component tool to be individually validated and approved. We believe, that our work can serve as a proof-of-concept for more advanced LLM-agents in the medical domain. © 2024, CC BY-NC-ND.

关键词： Autonomous agents

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Augmenting the fine beam tube: From hybrid measurements to magnetic field visualization

arXiv

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arXiv 2019年

作者： Bodensiek, O. Sonntag, D. Wendorff, N. Albuquerque, G. Magnor, M. Institute for Science Education Research Physics Group Technische Universität Braunschweig Computer Graphics Lab Technische Universität Braunschweig

This article may be downloaded for personal use only. Any other use requires permission of the author and AIP Publishing. The following article will appear in The Physics Teacher 57 (2019). Copyright © 2019, The ...

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Deep learning - A first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact

arXiv

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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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MFR 2021: Masked Face Recognition Competition

arXiv

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arXiv 2021年

作者： Boutros, Fadi Damer, Naser Kolf, Jan Niklas Raja, Kiran Kirchbuchner, Florian Ramachandra, Raghavendra Kuijper, Arjan Fang, Pengcheng Zhang, Chao Wang, Fei Montero, David Aginako, Naiara Sierra, Basilio Nieto, Marcos Erakın, Mustafa Ekrem Demir, Ugur Ekenel, Hazım Kemal Kataoka, Asaki Ichikawa, Kohei Kubo, Shizuma Zhang, Jie He, Mingjie Han, Dan Shan, Shiguang Grm, Klemen Štruc, Vitomir Seneviratne, Sachith Kasthuriarachchi, Nuran Rasnayaka, Sanka Neto, Pedro C. Sequeira, Ana F. Pinto, Joao Ribeiro Saffari, Mohsen Cardoso, Jaime S. Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Norwegian University of Science and Technology Norway TYAI China VICOMTECH Spain University of the Basque Country Spain SiMiT Lab Istanbul Technical University Turkey ACES Inc Japan Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China University of Ljubljana Slovenia University of Melbourne Australia University of Moratuwa Sri Lanka National University of Singapore Singapore INESC TEC Portugal University of Porto Portugal

This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed solutions by taking the compactness of the face recognition models into account. A private dataset representing a collaborative, multisession, real masked, capture scenario is used to evaluate the submitted solutions. In comparison to one of the top-performing academic face recognition solutions, 10 out of the 18 submitted solutions did score higher masked face verification accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Biometrics

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Behavioral Sentiment Analysis of Depressive States

Behavioral Sentiment Analysis of Depressive States

引用

International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Anna Esposito Gennaro Raimo Mauro Maldonato Carl Vogel Massimiliano Conson Gennaro Cordasco Universita degli Studi della Campania “L. Vanvitelli” International Institute for Advanced Scientific Studies (IIASS) Italy Universita di Napoli “Federico II” Naples Italy School of Computer Science and Statistics Trinity College Dublin Ireland

ISBN: (数字)9781728182131

ISBN: (纸本)9781728182148

The need to release accurate and incontrovertible diagnoses of depression has fueled the search for new methodologies to obtain more reliable measurements than the commonly adopted questionnaires. In such a context, research has sought to identify non-biased measures derived from analyses of behavioral data such as voice and language. For this purpose, sentiment analysis techniques were developed, initially based on linguistic characteristics extracted from texts and gradually becoming more and more sophisticated by adding tools for the analyses of voice and visual data (such as facial expressions and movements). This work summarizes the behavioral features accounted for detecting depressive states and sentiment analysis tools developed to extract them from text, audio, and video recordings.

关键词： Sentiment analysis Databases Tools Linguistics Depression Feature extraction Stress

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Frontal Left Alpha Activity as an Indicator of Willingness to Interact with Virtual Agents: A pilot study

Frontal Left Alpha Activity as an Indicator of Willingness t...

引用

International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Betul Tolgay Mauro Maldonato Carl Vogel Gennaro Cordasco Luigi Trojano Anna Esposito Universit'a degli Studi della Campania “L. Vanvitelli” Università di Napoli “Federico II” Naples Italy School of Computer Science and Statistics Trinity College Dublin Ireland International Institute for Advanced Scientific Studies (IIASS) Italy

ISBN: (数字)9781728182131

ISBN: (纸本)9781728182148

Over the last decade, much effort has been made to develop virtual agents acting as assistants of elderly people in their daily activities. With the emergence of such technologies, several questionnaires have been developed to investigate the factors increasing user's acceptance of virtual agents. While questionnaires provide detailed information about users' preferences, they may not be sufficient for investigating user's internal affective states and impressions during the interaction with virtual agents. Therefore, improving assessment techniques for elders' acceptance of virtual agents is necessary for understanding the impressions they arouse and determining their design accordingly. This paper is a report of a pilot study that benefits from the predictive ability of left frontal alpha activity in the brain on positive affect and approach related motivation, and investigates relationships between user's willingness to interact with virtual agents and left frontal alpha activity in order to gain insights on user's affective and motivational states during the interaction with an agent.

关键词： Assistive technology Conferences Senior citizens Aging Electroencephalography Real-time systems

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Spectrum of QCD with one flavor: A window for supersymmetric dynamics

arXiv

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arXiv 2023年

作者： Morte, Michele Della Jäger, Benjamin Sannino, Francesco Tsang, Justus Tobias Ziegler, Felix P.G. CP3-Origins Dept. of Mathematics and Computer Science University of Southern Denmark Odense M5230 Denmark Danish Institute for Advanced Study University of Southern Denmark Odense M5230 Denmark CP3-Origins Dept. of Physics Chemistry and Pharmacy University of Southern Denmark Odense M5230 Denmark Dipartimento di Fisica "E. Pancini" Università di Napoli Federico II INFN sezione di Napoli Complesso Universitario di Monte S. Angelo Napoli80126 Italy CERN Theoretical Physics Department Geneva 231211 Switzerland School of Physics and Astronomy The University of Edinburgh EdinburghEH9 3FD United Kingdom

We compute the spectrum of the low-lying mesonic states with vector, scalar and pseudoscalar quantum numbers in QCD with one flavour. With three colours the fundamental and the two-index anti-symmetric representations of the gauge group coincide. The latter is an orientifold theory that maps into the bosonic sector of N = 1 super Yang-Mills theory in the large number of colours limit. We employ Wilson fermions along with tree-level improvement in the gluonic and fermionic parts of the action. In this setup the Dirac operator can develop real negative eigenvalues. We therefore perform a detailed study in order to identify configurations where the fermion determinant is negative and eventually reweight them. We finally compare results with effective field theory predictions valid in the large NC limit and find reasonably consistent values despite NC being only three. Additionally, the spin-one sector provides a novel window for supersymmetric dynamics. © 2023, CC BY-NC-SA.

关键词： Eigenvalues and eigenfunctions

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Striped magnetization plateau and chirality-reversible anomalous Hall effect in a magnetic kagome metal

arXiv

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arXiv 2024年

作者： Cheng, Erjian Mao, Ning Yang, Xiaotian Song, Boqing Lou, Rui Ying, Tianping Nie, Simin Fedorov, Alexander Bertran, François Ding, Pengfei Suvorov, Oleksandr Zhang, Shu Changdar, Susmita Schnelle, Walter Koban, Ralf Yi, Changjiang Burkhardt, Ulrich Büchner, Bernd Wang, Shancai Zhang, Yang Wang, Wenbo Felser, Claudia Max Planck Institute for Chemical Physics of Solids Dresden01187 Germany School of Physical Science and Technology ShanghaiTech Laboratory for Topological Physics ShanghaiTech University Shanghai201210 China Institute of Physics University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100190 China Leibniz Institute for Solid State and Materials Research IFW Dresden Dresden01069 Germany Helmholtz-Zentrum Berlin für Materialien und Energie Albert-Einstein-Straße 15 Berlin12489 Germany Joint Laboratory "Functional Quantum Materials" BESSY II Berlin12489 Germany Department of Mechanical Engineering Stanford University StanfordCA94305 United States Synchrotron SOLEIL L'Orme des Merisiers Départementale 128 Saint-Aubin91190 France Department of Physics Key Laboratory of Quantum State Construction and Manipulation Ministry of Education Beijing Key Laboratory of Opto-electronic Functional Materials & Micronano Devices Renmin University of China Beijing100872 China Kyiv Academic University Kyiv03142 Ukraine Max Planck Institute for the Physics of Complex Systems Dresden01187 Germany Institute for Solid State and Materials Physics TU Dresden Dresden01062 Germany Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States

Kagome materials with magnetic frustration in two-dimensional networks are known for their exotic properties, such as the anomalous Hall effect (AHE) with non-collinear spin textures. However, the effects of one-dimensional (1D) spin chains within these networks are less understood. Here, we report a distinctive AHE in the bilayer-distorted kagome material GdTi3Bi4, featuring 1D Gd zigzag spin chains, a one-third magnetization plateau, and two successive metamagnetic transitions. At these metamagnetic transitions, Hall resistivity shows abrupt jumps linked to the formation of stripe domain walls, while within the plateau, the absence of detectable domain walls suggests possible presence of skyrmion phase. Reducing the sample size to a few microns reveals additional Hall resistivity spikes, indicating domain wall skew scattering contributions. Magnetic atomistic spin dynamics simulations reveal that the magnetic textures at these transitions have reverse chirality, explaining the evolution of AHE and domain walls with fields. These results underscore the potential of magnetic and crystal symmetry interplay, and magnetic field-engineered spin chirality, for controlling domain walls and tuning transverse properties, advancing spintronic applications. © 2024, CC BY.

关键词： Chirality

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Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data

arXiv

引用

arXiv 2019年

作者： Gsaxner, Christina Roth, Peter M. Wallner, Jürgen Egger, Jan Institute for 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 & Maxillofacial Surgery Medical University of Graz Auenbruggerplatz Styria Austria

We present an approach for fully automatic urinary bladder segmentation in CT images with artificial neural networks in this study. Automatic medical image analysis has become an invaluable tool in the different treatment stages of diseases. Especially medical image segmentation plays a vital role, since segmentation is often the initial step in an image analysis pipeline. Since deep neural networks have made a large impact on the field of image processing in the past years, we use two different deep learning architectures to segment the urinary bladder. Both of these architectures are based on pre-trained classification networks that are adapted to perform semantic segmentation. Since deep neural networks require a large amount of training data, specifically images and corresponding ground truth labels, we furthermore propose a method to generate such a suitable training data set from Positron Emission Tomography/Computed Tomography image data. This is done by applying thresholding to the Positron Emission Tomography data for obtaining a ground truth and by utilizing data augmentation to enlarge the dataset. In this study, we discuss the influence of data augmentation on the segmentation results, and compare and evaluate the proposed architectures in terms of qualitative and quantitative segmentation performance. The results presented in this study allow concluding that deep neural networks can be considered a promising approach to segment the urinary bladder in CT images. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Visual Analysis of Urban Traffic Data based on High-Resolution and High-Dimensional Environmental Sensor Data 6

Visual Analysis of Urban Traffic Data based on High-Resoluti...

引用

6th Workshop on Visualisation in Environmental sciences, EnvirVis 2018 at EuroVis 2018

作者： Häußler, Johannes Stein, Manuel Seebacher, Daniel Janetzko, Halldor Schreck, Tobias Keim, Daniel A. University of Konstanz Department of Computer and Information Science Germany University of Zurich Department of Geography Switzerland Graz University of Technology Institute for Computer Graphics and Knowledge Visualization Austria

ISBN: (纸本)9783038680635

Urbanization is an increasing global trend resulting in a strong increase in public and individual transportation needs. Accordingly, a major challenge for traffic and urban planners is the design of sustainable mobility concepts to maintain and increase the long-term health of humans by reducing environmental pollution. Recent developments in sensor technology allow the precise tracking of vehicle sensor information, allowing a closer and more in-depth analysis of traffic data. We propose a visual analytics system for the exploration of environmental factors in these high-resolution and high-dimensional mobility sensor data. Additionally, we introduce an interactive visual logging approach to enable experts to cope with complex interactive analysis processes and the problem of the reproducibility of results. The usefulness of our approach is demonstrated via two expert studies with two domain experts from the field of environment-related projects and urban traffic planning. © 2018 The Author(s) Eurographics Proceedings © 2018 The Eurographics Association.

关键词： computer graphics

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