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arXiv 2020年

作者： Egger, Jan Gsaxner, Christina Pepe, Antonio Pomykala, Kelsey L. Jonske, Frederic Kurz, Manuel Li, Jianning Kleesiek, Jens Institute of Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Inffeldgasse 16 Styria Graz8010 Austria Department of Oral & Maxillofacial Surgery Medical University of Graz Auenbruggerplatz 5/1 Styria Graz8036 Austria Computer Algorithms for Medicine Laboratory Styria Graz Austria University Medicine Essen Girardetstraße 2 Essen45131 Germany University Medicine Essen Hufelandstraße 55 Essen45147 Germany Partner Site Essen Hufelandstraße 55 Essen45147 Germany

Deep learning has remarkably impacted several different scientific disciplines over the last few years. For example, in image processing and analysis, deep learning algorithms were able to outperform other cutting-edge methods. Additionally, deep learning has delivered state-of-the-art results in tasks like autonomous driving, outclassing previous attempts. There are even instances where deep learning outperformed humans, for example with object recognition and gaming. Deep learning is also showing vast potential in the medical domain. With the collection of large quantities of patient records and data, and a trend towards personalized treatments, there is a great need for automated and reliable processing and analysis of health information. Patient data is not only collected in clinical centers, like hospitals and private practices, but also by mobile healthcare apps or online websites. The abundance of collected patient data and the recent growth in the deep learning field has resulted in a large increase in research effort s. In Q2/2020, the search engine PubMed returned already over 11,000 results for the search term 'deep learning', and around 90% of these publications are from the last three years. However, even though PubMed represents the largest search engine in the medical field, it does not cover all medical-related publications. Hence, a complete overview of the field of 'medical deep learning' is almost impossible to obtain and acquiring a full overview of medical sub-fields is becoming increasingly more difficult. Nevertheless, several review and survey articles about medical deep learning have been published within the last few years. They focus, in general, on specific medical scenarios, like the analysis of medical images containing specific pathologies. With these surveys as a foundation, the aim of this article is to provide the first high-level, systematic meta-review of medical deep learning surveys. Copyright © 2020, The Authors. All rights res

关键词： Generative adversarial networks

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CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosis

arXiv

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arXiv 2022年

作者： Prazuch, Wojciech Suwalska, Aleksandra Socha, Marek Tobiasz, Joanna Foszner, Pawel Jaroszewicz, Jerzy Gruszczynska, Katarzyna Sliwinska, Magdalena Walecki, Jerzy Popiela, Tadeusz Przybylski, Grzegorz Cieszanowski, Andrzej Nowak, Mateusz Pawlowska, Malgorzata Flisiak, Robert Simon, Krzysztof Zapolska, Gabriela Gizycka, Barbara Szurowska, Edyta Marczyk, Michal Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice Poland Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Gliwice Poland Department of Infectious Diseases and Hepatology Medical University of Silesia Katowice Poland Department of Diagnostic Imaging Voivodship Specialist Hospital Wroclaw Poland Department of Diagnostic Radiology Central Clinical Hospital of The Ministry of Internal Affairs and Administration Warsaw Poland Department of Radiology Jagiellonian University Medical College Krakow Poland Department of Lung Diseases Cancer and Tuberculosis Kujawsko-Pomorskie Pulmonology Center Bydgoszcz Poland Department of Imaging Diagnostics National Institute of Oncology Warsaw Poland Department of Radiology Silesian Hospital Cieszyn Poland Department of Infectious Diseases and Hepatology Collegium Medicum in Bydgoszcz Nicolaus Copernicus University Torun Poland Department of Infectious Diseases and Hepatology Medical University of Bialystok Bialystok Poland Department of Infectious Diseases and Hepatology Wroclaw Medical University Wroclaw Poland Department of Radiology Czerniakowski Hospital Warsaw Poland PDepartment of Imaging Diagnostics MEGREZ Hospital Tychy Poland 2nd Department of Radiology Medical University of Gdansk Poland Yale Cancer Center Yale School of Medicine New HavenCT United States

Background: Due to the large accumulation of patients requiring hospitalization, the COVID-19 pandemic disease caused a high overload of health systems, even in developed countries. Deep learning techniques based on medical imaging data can help in the faster detection of COVID-19 cases and monitoring of disease progression. However, regardless of the numerous proposed solutions using lung X-rays, none of them is a product that can be used in the clinic. Methods: Five different datasets (clinical POLCOVID and AIforCOVID databases, a public collection of images called COVIDx set, pneumonia patients from NIH, and artificially generated data) were used to construct a representative dataset of 23 799 CXRs for model training, 1 050 images as a hold-out test set and 44 247 CXRs as independent test set (BIMCV database). A U-Net-based model was developed to identify a clinically relevant region of the CXR that contains the lungs and each class (normal, pneumonia, and COVID-19) was divided into 3 subtypes using a 2D Gaussian mixture model. A decision tree was used to aggregate predictions from the InceptionV3 convolutional network based on processed CXRs and a dense 7-layer neural network on radiomic features determined independently for the upper, middle, and lower lung fragments. Results: The lung segmentation model gave the Sorensen-Dice coefficient of 94.86% in the validation dataset, and 93.36% in the testing dataset. In 5-fold cross-validation, the accuracy for all classes ranged from 91% to 93%, keeping slightly higher specificity than sensitivity. Also, NPV was higher than PPV for each class to guarantee a low rate of false negative cases. In the hold-out test set, the balanced accuracy ranged between 68% and 100%. The highest performance was obtained for the subtypes N1, P1, and C1, while images assigned to subtypes N3, P3, or C3 have been wrongly predicted among themselves. A similar performance was obtained on the independent dataset for normal and COVID-19 class

关键词： COVID-19

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Cross-Domain conditional generative adversarial networks for stereoscopic hyperrealism in surgical training

arXiv

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arXiv 2019年

作者： Engelhardt, Sandy Sharan, Lalith Karck, Matthias Simone, Raaele De Wolf, Ivo Faculty of Computer Science Mannheim University of Applied Sciences Department of Cardiac Surgery Heidelberg University Hospital Dep. of Simulation and Graphics Magdeburg University

Phantoms for surgical training are able to mimic cutting and suturing properties and patient-individual shape of organs, but lack a realistic visual appearance that captures the heterogeneity of surgical scenes. In order to overcome this in endoscopic approaches, hyperreal- istic concepts have been proposed to be used in an augmented reality- setting, which are based on deep image-to-image transformation meth- ods. Such concepts are able to generate realistic representations of phan- toms learned from real intraoperative endoscopic sequences. Conditioned on frames from the surgical training process, the learned models are able to generate impressive results by transforming unrealistic parts of the image (e.g. the uniform phantom texture is replaced by the more hetero- geneous texture of the tissue). Image-to-image synthesis usually learns a mapping G : X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y . However, it does not necessarily force the generated images to be consistent and without ar- tifacts. In the endoscopic image domain this can affect depth cues and stereo consistency of a stereo image pair, which ultimately impairs surgi- cal vision. We propose a cross-domain conditional generative adversarial network approach (GAN) that aims to generate more consistent stereo pairs. The results show substantial improvements in depth perception and realism evaluated by 3 domain experts and 3 medical students on a 3D monitor over the baseline method. In 84 of 90 instances our proposed method was preferred or rated equal to the baseline. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Guest editors' introduction: Special section on IEEE PacificVis 2019

IEEE Pacific Visualization Symposium

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IEEE Pacific Visualization Symposium 2019年 2019-April卷 XIV-XV页

作者： Maciejewski, Ross Seo, Jinwook Westermann, Rüdiger School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Department of Computer Science and Engineering Seoul National University Seoul Korea Republic of Computer Graphics and Visualization Group Technische Universitat Munchen Garching Germany

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Graph-based parallel large scale structure from motion

arXiv

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arXiv 2019年

作者： Chen, Yu Shen, Shuhan Chen, Yisong Wang, Guoping Graphics and Interactive Lab Department of Computer Science and Technology School of Electronic Engineering and Computer Science Peking University National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

While Structure from Motion (SfM) achieves great success in 3D reconstruction, it still meets challenges on large scale scenes. In this work, large scale SfM is deemed as a graph problem, and we tackle it in a divide-and-conquer manner. Firstly, the images clustering algorithm divides images into clusters with strong connectivity, leading to robust local reconstructions. Then followed with an image expansion step, the connection and completeness of scenes are enhanced by expanding along with a maximum spanning tree. After local reconstructions, we construct a minimum spanning tree (MST) to find accurate similarity transformations. Then the MST is transformed into a Minimum Height Tree (MHT) to find a proper anchor node and is further utilized to prevent error accumulation. When evaluated on different kinds of datasets, our approach shows superiority over the state-of-the-art in accuracy and efficiency. Our algorithm is open-sourced in https://***/AIBluefisher/GraphSfM. Copyright © 2019, The Authors. All rights reserved.

关键词： Clustering algorithms

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Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks

arXiv

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arXiv 2021年

作者： Grandits, Thomas Pezzuto, Simone Costabal, Francisco Sahli Perdikaris, Paris Pock, Thomas Plank, Gernot Krause, Rolf Institute of Computer Graphics and Vision TU Graz Graz Austria BioTechMed-Graz Graz Austria Center for Computational Medicine in Cardiology Institute of Computational Science Università della Svizzera italiana Lugano Switzerland Department of Mechanical and Metallurgical Engineering School of Engineering Pontificia Universidad Católica de Chile Santiago Chile Institute for Biological and Medical Engineering Schools of Engineering Medicine and Biological Sciences Pontificia Universidad Católica de Chile Santiago Chile Millennium Nucleus for Cardiovascular Magnetic Resonance Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania PhiladelphiaPA United States Gottfried Schatz Research Center Division of Biophysics Medical University of Graz Graz Austria

Electroanatomical maps are a key tool in the diagnosis and treatment of atrial fibrillation. Current approaches focus on the activation times recorded. However, more information can be extracted from the available data. The fibers in cardiac tissue conduct the electrical wave faster, and their direction could be inferred from activation times. In this work, we employ a recently developed approach, called physics informed neural networks, to learn the fiber orientations from electroanatomical maps, taking into account the physics of the electrical wave propagation. In particular, we train the neural network to weakly satisfy the anisotropic eikonal equation and to predict the measured activation times. We use a local basis for the anisotropic conductivity tensor, which encodes the fiber orientation. The methodology is tested both in a synthetic example and for patient data. Our approach shows good agreement in both cases, with an RMSE of 2.2ms on the in-silico data and outperforming a state of the art method on the patient data. The results show a first step towards learning the fiber orientations from electroanatomical maps with physics-informed neural networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Tensors

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Authenticating Users Through Fine-Grained Channel Information

Authenticating Users Through Fine-Grained Channel Informatio...

引用

作者： Liu, Hongbo Wang, Yan Liu, Jian Yang, Jie Chen, Yingying Poor, H. Vincent Department of Computer Information and Graphics Technology Indiana University-Purdue University Indianapolis IndianapolisIN46222 United States Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States Department of Computer Science Florida State University TallahasseeFL32306 United States Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

User authentication is the critical first step in detecting identity-based attacks and preventing subsequent malicious attacks. However, the increasingly dynamic mobile environments make it harder to always apply cryptographic-based methods for user authentication due to their infrastructural and key management overhead. Exploiting non-cryptographic based techniques grounded on physical layer properties to perform user authentication appears promising. In this work, the use of channel state information (CSI), which is available from off-the-shelf WiFi devices, to perform fine-grained user authentication is explored. Particularly, a user-authentication framework that can work with both stationary and mobile users is proposed. When the user is stationary, the proposed framework builds a user profile for user authentication that is resilient to the presence of a spoofer. The proposed machine learning based user-authentication techniques can distinguish between two users even when they possess similar signal fingerprints and detect the existence of a spoofer. When the user is mobile, it is proposed to detect the presence of a spoofer by examining the temporal correlation of CSI measurements. Both office building and apartment environments show that the proposed framework can filter out signal outliers and achieve higher authentication accuracy compared with existing approaches using received signal strength (RSS). © 2017 IEEE.

关键词： Channel state information

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Elastic mitral valve silicone replica made from 3D-printable molds offer advanced surgical training

Elastic mitral valve silicone replica made from 3D-printable...

引用

Workshop on Bildverarbeitung fur die Medizin, 2018

作者： Engelhardt, Sandy Sauerzapf, Simon Al-Maisary, Sameer Karck, Matthias Preim, Bernhard Wolf, Ivo De Simone, Raffaele Faculty for Computer Science Mannheim University of Applied Sciences Mannheim Germany Department of Simulation and Graphics Magdeburg University Magdeburg Germany Departement of Cardiac Surgery Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783662580950

Reconstructive mitral valve surgeries are demanding cardiac surgeries that are conducted in the mid-age of a surgical career at the earliest. Receiving years of training in patients is required to gain the experiences and skills necessary for surgical success. On top of that, the number of such surgeries is limited per hospital, therefore other means of training should be offered to facilitate a steep learning curve. Within the scope of this work, we equipped an existing physical simulator with patient-specific flexible replica of the mitral valve. We developed software to automatically produce a 3D-printable casting mold for silicone material, as this mimics properties of the valve tissue in terms of stitching and cutting. We show the feasibility of the approach and the usefulness of these models is evaluated by an experienced cardiac surgeon, successfully conducting reconstructive surgery on pathological silicone valves. © Springer-Verlag GmbH Deutschland 2018.

关键词： Silicones

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Performance Analysis of a Cloud Computing System using Queuing Model with Correlated Task Reneging

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Journal of Physics: Conference Series 2021年第1期2091卷

作者： Rakesh Kumar Bhavneet Singh Soodan Godlove Suila Kuaban Piotr Czekalski Sapana Sharma School of Mathematics Shri Mata Vaishno Devi University Katra Jammu and Kashmir 182320 India Department of Mathematics and Statistics Namibia University of Science and Technology Private Bag 13388 Windhoek Namibia 13 Jackson Kaujeua Street Windhoek Namibia Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 44-100 Gliwice Poland Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Department of Mathematics Cluster University of Jammu Jammu and Kashmir

Queuing theory has been extensively used in the modelling and performance analysis of cloud computing systems. The phenomenon of the task (or request) reneging, that is, the dropping of requests from the request queue often occur in cloud computing systems, and it is important to consider it when developing performance evaluations models for cloud computing infrastructures. Majority of studies in the performance evaluation of cloud computing data centres with the use of queuing theory do not consider the fact that the tasks could be removed from queue without being serviced. The removal of tasks from the queue could be due to the user impatience, execution deadline expiration, security reasons, or as an active queue management strategy. The reneging could be correlated in nature, that is, if a request is dropped (or reneged) at any time epoch, and then there is a probability that a request may or may not be dropped at the next time epoch. This kind of dropping (or reneging) of requests is referred to as correlated request reneging. In this paper we have modelled a cloud computing infrastructure with correlated request reneging using queuing theory. An M/M/1/N queuing model with correlated reneging has been used to study the performance analysis of the load balancing server of a cloud computing system. The steady-state as well as the transient performance analyses have been carried out. Important measures of performance like average queue size, average delay, probability of task blocking, and the probability of no waiting in the queue are studied. Finally, some comparisons are performed which describe the effect of correlated task reneging over simple exponential reneging.

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3D-print, virtual reality or regular screen? comparison of presentation modalities for 3D liver reconstructions

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HPB 2021年 23卷 S781-S781页

作者： Huettl, F. Saalfeld, P. Hansen, C. Preim, B. Poplawski, A. Kneist, W. Lang, H. Huber, T. University Medical Center of the Johannes Gutenberg-University Mainz Department of General Visceral and Transplant Surgery Germany Otto-von-Guericke University Magdeburg Institute of Simulation and Graphics Faculty of Computer Science Germany University Medical Center of the Johannes Gutenberg-University Mainz Institute of Medical Biostatistics Epidemiology and Informatics (IMBEI) Germany

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