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AB1556 MACHINE LEARNING APPROACH FOR CLASSIFICATION OF ARTHRITIS ACTIVITY STATE, USING DATA FROM A SINGLE ACCELEROMETER

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Annals of the Rheumatic Diseases 2023年 82卷 2012-2012页

作者： P. Mielnik A. Myhre Hjelle A. Traseth K. Tokarz B. Pollen M. Fojcik Helse Forde Section for Rheumatology； Department for Neurology Rheumatology and Physical Medicine Forde Norway Institute of Informatics Silesian University of Technology Department of Graphics Computer Vision and Digital Systems Gliwice Poland Western Norway University of Applied Sciences Faculty of Engineering and Science Forde Norway

Background Machine learning (ML) potential is not fully exploited in diagnostics and follow up of autoimmune inflammatory rheumatic disease (AIIRD). It is despite the broader use of ML in, e.g. imaging diagnostics. The specific tools for AIIRD are lacking. Objectives This is an interim analysis of data from the first checkpoint of the proof-of-concept study on using accelerometer (ACC) data in follow up of patients with AIIRD. The main goal of the study was to investigate the value of single ACC data in the classification of arthritis activity status. Methods Subjects with AIIRD are enrolled in the study when they start of new treatment due to the disease activity. Several comorbidities, such as severe neurological and cardiological disorders, as wells as impaired mobility, are part of the protocol exclusion criteria. Volunteers without AIIRD who fulfil other inclusion/exclusion criteria are included as controls. The study was approved by local competent authorities and informed consent was given by all participants. We used data collected up till the first study checkpoint. This analysis covers nine patients with AIIRD (5 rheumatoid and four psoriasis arthritis) and 13 controls. Five patients had 3 study visits, 1 had 2 visits and 3 only one. Controls have only one visit per protocol. We analysed ACC data from 3 minutes of clapping using a home-brewed device based on Arduino nano 33 BLE with 6-axis MTU by Nordic Semiconductor. Data was divided into 6-second chunks that were found optimal in our prior study. We conducted binary classification between any/ no arthritis in any of the upper extremities. We used accuracy and area under curve (AUC) as an efficacy function derived from the receiver operating characteristic curve (ROC). We extracted 54 features from 3 ACC axis signal. The features encompassed, among others, Fourier's components, autoregression coefficients, median absolute deviation (MAD), variance, Fourier's entropy, etc. We built linear discriminant anal

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Depth-Awareness in a System for Mixed-Reality Aided Surgical Procedures 15th

Depth-Awareness in a System for Mixed-Reality Aided Surgical...

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15th International Conference on Intelligent Computing, ICIC 2019

作者： Sylos Labini, Mauro Gsaxner, Christina Pepe, Antonio Wallner, Juergen Egger, Jan Bevilacqua, Vitoantonio 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 Electrical and Information Engineering Polytechnic University of Bari Bari Italy Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria

ISBN: (纸本)9783030267650

computer-assisted surgery is a trending topic in research, with many different approaches which aim at supporting surgeons in the operating room. Existing surgical planning and navigation solutions are often considered to be distracting, unintuitive or hard to interpret. In this work, we address this issue with an approach based on mixed reality devices like Microsoft HoloLens. We assess the depth sensing capabilities of Microsoft HoloLens, and the potential benefit they could bring to computer-assisted surgery applications. © 2019, Springer Nature Switzerland AG.

关键词： Mixed reality

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Exploring Novel Ai-Based Approaches for Plant Features Extraction in Image Datasets with Small Size: The Case Study of Nitrogen Estimation in Sorghum Using Uav-Based Rgb Sensing

SSRN

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SSRN 2023年

作者： Hammouch, Hajar Patil, Suchitra El-Yacoubi, Mounim A.A. Masner, Jan Kholová, Jana Choudhary, Sunita Anbazhagan, Krithika Vaněk, Jiří Qin, Huafeng Stočes, Michal Berbia, Hassan Jagarlapudi, Adinarayana Chandramouli, Magesh Mamidi, Srinivas Prasad, K.V.S.V. Baddam, Rekha SAMOVAR Telecom SudParis Institut Polytechnique de Paris Palaiseau91120 France Mohamed V University Rabat Morocco Centre of Studies in Resources Engineering Indian Institute of Technology Bombay Powai Maharashtra Mumbai400076 India Telangana Patancheru502 324 India Department of Information Technology K. J. Somaiya College of Engineering Vidyavihar Maharashtra Mumbai400 077 India Department of Information Technologies Czech University of Life Sciences Prague Prague165 00 Czech Republic Chongqing University China Computer Graphics Technology Purdue University NW HammondIN46323 United States Marut Dronetech Private Limited Gachibowli Telangana Hyderabad500032 India Patancheru Hyderabad502 324 India

This study was primarily triggered by the requirements of crop breeding programs to rapidly, accurately and cost-effectively select crop genotypes with enhanced biomass Nitrogen (N) content (an essential indicator of stover quality as feed) and, at the same time, possibly speed up the agriculture transition to the more sustainable and precise application of fertilizers (crop health status). This proof of concept study explores whether and how precisely it is possible to predict the N content in sorghum crop biomass using a minimum unmanned aerial vehicle (UAV)-carried imaging technology (Red Green Blue (RGB) camera) and a small dataset. To this end, we raised a post-rainy (rabi) 2021 and 2022 season sorghum crop in the field with standard and sub-optimal management practices (N and water stress) at the International Crop Research institute for Semi-arid Tropics (ICRISAT), India. RGB images were collected during different crop growth stages (rabi 2021, 2022) using a low-cost RGB camera DJI Zenmuse X5S mounted UAV DJI Matrice 210 *** collected three ground-truth points with corresponding RGB images to build the N-prediction model (rabi 2021) and then six ground-truth points with corresponding RGB to evaluate the model with a second independent test set (rabi 2022). We have assessed statistical and AI-based models for N prediction, namely multiple linear regression (MLR), multi-layer perceptron (MLP) and various convolutional neural network (CNN) architectures, as well as their combinations. The first two take as input five handcrafted features computed using RGB spectrum vegetation indices (VIs) while the CNN automatically extracts discriminant features from the raw images. These schemes have already been proposed in state-of-the-art and the novelty of our study lies in the assessment of several fusion strategies in order to leverage both the deep features learned automatically by the CNN and the human-expertise-based handcrafted features, harnessing, thereb

关键词： Crops

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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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Functional voxel representation of the Voronoi diagram as a tool for analyzing the structure of interparticle space

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AIP Conference Proceedings 2022年第1期2503卷

作者： Mikhail Loktev 1Laboratory of Computer Graphics V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences 65 Profsoyuznaya street Moscow 117997 Russia 2Department of Engineering Graphics Moscow State Technological University “STANKIN” Vadkovsky Pereulok 3a. 127055 Moscow Russia

The article discusses an approach to research in the field of interpore space analysis, which is primarily geometric in nature. The method of functional-voxel modeling is used as a basic geometric tool, since it allows you to form graphic M-images (model image) of any dimension. For the analytical representation of the geometry and Voronoi diagram of the model, the mathematical apparatus of R-functions (Rvachev functions) is used. A software implementation of the proposed approaches for constructing a Voronoi diagram in three-dimensional space will be presented.

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Energy Adaptive Convolution Neural Network Using Dynamic Partial Reconfiguration

Energy Adaptive Convolution Neural Network Using Dynamic Par...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Eman Youssef Hamed A. Elsemary Magdy A. El-Moursy Ahmed Khattab Hassan Mostafa Microelectronics Department Electronics Research Institute (ERI) Cairo Egypt Computer Engineering Dept Prince Sattam Bin Abdulaziz University KSA Design Creation Division Mentor Graphics Cairo Egypt Electronics and Communications Engineering Faculty of Engineering Cairo University Giza Egypt

ISBN: (数字)9781728180588

ISBN: (纸本)9781728180595

Convolutional Neural Network (CNN) is a good candidate for computer vision applications. CNN is well known for its great classification accuracy at image recognition tasks. The cost of CNN is its large power consumption as it needs a lot of multiplication and addition operations. Approximation can reduce the power consumption. CNNs can be implemented by CPUs, GPUs or FPGAs. In this paper, the proposed CNN is implemented on Xilinx XC7Z020 FPGA and is trained to recognize MNIST dataset This CNN is approximated through quantization which reduces the accuracy only by 0.53% while using 7-bits for the implementation. A reduction of 2.7X is achieved in energy consumption compared to the conventional design which uses 16-bits. Dynamic Partial Reconfiguration (DPR) reconfigures the FPGA during the run time with appropriate power consumption design if the battery level decreases. This enables recognition applications to run with low power budget but with sacrificing minor accuracy instead of termination.

关键词： Field programmable gate arrays Convolution Hardware Biological neural networks Training Image recognition Neurons

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Apple Vision Pro for Healthcare: "The Ultimate Display"? Entering the Wonderland of Precision Medicine

arXiv

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arXiv 2023年

作者： Egger, Jan Gsaxner, Christina Chen, Xiaojun Bian, Jiang Kleesiek, Jens Puladi, Behrus Girardetstraße 2 Essen45131 Germany Hufelandstraße 55 Essen45147 Germany Hufelandstraße 55 Essen45147 Germany Department of Oral and Maxillofacial Surgery University Hospital RWTH Aachen Pauwelsstraße 30 Aachen52074 Germany Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16/II Graz8010 Austria Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Dongchuan Road 800 Minhang District Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Dongchuan Road 800 Minhang District Shanghai200240 China Health Outcomes & Biomedical Informatics College of Medicine University of Florida GainesvilleFL32610 United States Partner Site Essen Hufelandstraße 55 Essen45147 Germany Department of Physics TU Dortmund University August-Schmidt-Str. 4 Dortmund44227 Germany Institute of Medical Informatics University Hospital RWTH Aachen Pauwelsstraße 30 Aachen52074 Germany

At the Worldwide Developers Conference (WWDC) in June 2023, Apple introduced the Vision Pro. The Vision Pro is a Mixed Reality (MR) headset, more specifically it is a Virtual Reality (VR) device with an additional Video See-Through (VST) capability. The VST capability turns the Vision Pro also into an Augmented Reality (AR) device. The AR feature is enabled by streaming the real world via cameras to the (VR) screens in front of the user’s eyes. This is of course not unique and similar to other devices, like the Varjo XR-3. Nevertheless, the Vision Pro has some interesting features, like an inside-out screen that can show the headset wearers’ eyes to "outsiders" or a button on the top, called "Digital Crown", that allows you to seamlessly blend digital content with your physical space by turning it. In addition, it is untethered, except for the cable to the battery, which makes the headset more agile, compared to the Varjo XR-3. This could actually come closer to the "Ultimate Display", which Ivan Sutherland had already sketched in 1965. Not available to the public yet, like the Ultimate Display, we want to take a look into the crystal ball in this perspective to see if it can overcome some clinical challenges that - especially - AR still faces in the medical domain, but also go beyond and discuss if the Vision Pro could support clinicians in essential tasks to spend more time with their patients. © 2023, CC BY.

关键词： Augmented reality

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Minimal Solutions for Relative Pose With a Single Affine Correspondence

Minimal Solutions for Relative Pose With a Single Affine Cor...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Banglei Guan Ji Zhao Zhang Li Fang Sun Friedrich Fraundorfer College of Aerospace Science and Engineering National University of Defense Technology China National University of Defense Technology Changsha China Institute for Computer Graphics and Vision Graz University of Technology Austria Remote Sensing Technology Institute German Aerospace Center Germany

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

In this paper we present four cases of minimal solutions for two-view relative pose estimation by exploiting the affine transformation between feature points and we demonstrate efficient solvers for these cases. It is shown, that under the planar motion assumption or with knowledge of a vertical direction, a single affine correspondence is sufficient to recover the relative camera pose. The four cases considered are two-view planar relative motion for calibrated cameras as a closed-form and a least-squares solution, a closed-form solution for unknown focal length and the case of a known vertical direction. These algorithms can be used efficiently for outlier detection within a RANSAC loop and for initial motion estimation. All the methods are evaluated on both synthetic data and real-world datasets from the KITTI benchmark. The experimental results demonstrate that our methods outperform comparable state-of-the-art methods in accuracy with the benefit of a reduced number of needed RANSAC iterations.

关键词： Cameras Pose estimation Transmission line matrix methods Robot vision systems Simultaneous localization and mapping Motion estimation

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ConceptExplorer: Visual Analysis of Concept Drifts in Multi-source Time-series Data

ConceptExplorer: Visual Analysis of Concept Drifts in Multi-...

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IEEE Symposium on Visual Analytics Science and Technology (VAST)

作者： Xumeng Wang Wei Chen Jiazhi Xia Zexian Chen Dongshi Xu Xiangyang Wu Mingliang Xu Tobias Schreck State Key Lab of CAD&CG Zhejiang University School of Computer Science and Engineering Central South University College of Information Engineering China Jiliang University Institute of Graphics and Image Hangzhou Dianzi University Zhengzhou University Graz University of Technology

ISBN: (数字)9781728180090

ISBN: (纸本)9781728180106

Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features from multiple data sources. This paper proposes a novel visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series. We propose a visual detection scheme for discovering concept drifts from multiple sourced time-series based on prediction models. We design a drift level index to depict the dynamics, and a consistency judgment model to justify whether the concept drifts from various sources are consistent. Our integrated visual interface, ConceptExplorer, facilitates visual exploration, extraction, understanding, and comparison of concepts and concept drifts from multi-source time-series data. We conduct three case studies and expert interviews to verify the effectiveness of our approach.

关键词： Predictive models Data visualization Data models Adaptation models Indexes Analytical models Visual analytics

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IRIS: Interactive real-time feedback image segmentation with deep learning

IRIS: Interactive real-time feedback image segmentation with...

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Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Pepe, Antonio Schussnig, Richard Li, Jianning Gsaxner, Christina Chen, Xiaojun Fries, Thomas-Peter Egger, Jan Stanford University School of Medicine Department of Radiology 300 Pasteur Dr StanfordCA United States Graz University of Technology Institute of Computer Graphics and Vision Inffeldgasse 16c/II GrazA-8010 Austria Computer Algorithms for Medicine Laboratory GrazA-8010 Austria Graz University of Technology Institute of Structural Analysis Lessingstraße 25/II GrazA-8010 Austria Medical University of Graz Department of Oral and Maxillofacial Surgery Auenbruggerplatz 5/1 GrazA-8036 Austria Shanghai Jiao Tong University School of Mechanical Engineering 800 Dong Chuan Road Shanghai200240 China

ISBN: (纸本)9781510634015

Volumetric examinations of the aorta are nowadays of crucial importance for the management of critical pathologies such as aortic dissection, aortic aneurism, and other pathologies, which affect the morphology of the artery. These examinations usually begin with the acquisition of a Computed Tomography Angiography (CTA) scan from the patient, which is later on postprocessed to reconstruct the 3D geometry of the aorta. The first postprocessing step is referred to as segmentation. Different algorithms have been suggested for the segmentation of the aorta;including interactive methods, as well as fully automatic methods. Interactive methods need to be fine-tuned on each single CTA scan and result in longer duration of the process, whereas fully automatic methods require the possession of a large amount of labeled training data. In this work, we introduce a hybrid approach by combining a deep learning method with a consolidated interaction technique. In particular, we trained a 2D and a 3D U-Net on a limited number of patches extracted from 25 labeled CTA scans. Afterwards, we use an interactive approach, which consists in defining a region of interest (ROI) by just placing a seed point. This seed point is later used as the center of a 2D or 3D patch to be fed to the 2D or 3D U-Net, respectively. Due to the low content variation of these patches, this method allows to correctly segment the ROIs without the need for parameter tuning for each dataset and with a smaller training dataset, requiring the same minimal interaction as state-of-the-art interactive methods. Later on, the new segmented CTA scans can be further used to train a convolutional network for a fully automatic approach. © 2020 SPIE

关键词： Angiography

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