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Anatomical Landmarks Annotation on 2D Lateral Cephalograms with Channel Attention 22

Anatomical Landmarks Annotation on 2D Lateral Cephalograms w...

引用

22nd ieee/ACM International symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Du, Dongfeng Ren, Tao Chen, Chen Jiang, Yiran Song, Guangying Li, Qingfeng Niu, Jianwei Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Sch Comp Sci & Engn Beijing 100081 Peoples R China Peking Univ Dept Orthodont Sch & Hosp Stomatol Beijing 100081 Peoples R China

ISBN: (纸本)9781665499569

Cephalometric tracing is widely used in orthodontic diagnosis and treatment planning. Since manual landmark localization suffers from severe inter-observer and intra-observer inconsistency, a large number of efforts have been made by researchers to develop automatic localization methods. However, most of the existing methods are developed based on rules which sample uniformly from origin images rather than with the highest density in a focal point and ignore intermediate layers' results in their networks or their outputs' channels. To address the issue, this paper proposes a deep learning model based on multi-scale and multi-channel attention to identify landmarks. The channel attention network is first trained by multi-scale image patches cropped from 100 Cephalograms, and then enhanced by cross-layer connections to extract high-level features, finally involved in the collaboration with a three-layer MLP module to accurately locate coordinates. We conduct extensive evaluation on a real cephalometric X-ray data-set and a Non-public dataset, both achieve promising performance improvements especially in terms of high-precision detection.

关键词： Deep learning landmark identification attention mechanism 2D X-ray cephalometric analysis smart detection

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Voice-Specific Augmentations for Parkinson's Disease Detection Using Deep Convolutional Neural Network 20

Voice-Specific Augmentations for Parkinson's Disease Detecti...

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20th ieee Jubilee World symposium on Applied Machine Intelligence and Informatics, SAMI 2022

作者： Hires, Mate Gazda, Matej Vavrek, Lukas Drotar, Peter Technical University of Košice Faculty of Electrical Engineering and Informatics Department of Computers and Informatics Slovakia Technical University of Košice Faculty of Electrical Engineering and Informatics Department of Mathematics and Theoretical Informatics Slovakia

ISBN: (纸本)9781665497046

Deep convolutional neural networks (CNN) have provided remarkable results in speech processing tasks. However, they require a large amount of training data to prevent overfitting. data augmentation is commonly used to increase the quantity of training data by altering the original samples. Furthermore, it improves the generalization and robustness of CNN models. This work provides a deep learning approach for detecting Parkinson's disease (PD) from voice recordings converted to spectrograms. We investigate the use of six voice-specific data augmentation techniques in PD detection to improve generalization to unseen data. We utilized the Xception model to evaluate the performance of the proposed approach. We separately analyzed the articulatory and phonatory aspects of the voice of PD patients by considering recordings of vowels and words from the PC-GITA dataset. The best results were achieved on vowel data. Our model yielded 92.33% accuracy in the case of the /a/ vowel subset. In the case of words, the model achieved 82.12% accuracy on the whole words set. The obtained results show that voice-specific augmentation techniques improve the detection of speech impairments in PD patients. © 2022 ieee.

关键词： Training Training data Robustness Recording Convolutional neural networks Task analysis Speech processing

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Generating Sentinel-1 WV-Mode Quicklooks via Deep Learning Polarimetric Information Reconstruction

Generating Sentinel-1 WV-Mode Quicklooks via Deep Learning P...

引用

ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Leonardo De Laurentiis Clément Albinet ESA-ESRIN Frascati Italy

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

In this study we present some preliminary achievements on our proposed deep learning framework specifically designed to perform Sentinel-1 polarimetric information reconstruction. In particular, we aim at demonstrating our deep learning framework ability to generate missing polarization information from single-polarization datasets, with a view to supporting the final generation of useful quicklooks also starting from single-polarization Sentinel-1 data. After a robust validation, the deep learning framework may prove effective in both qualitative and quantitative assessments based on reconstructed quicklooks, notably for the Sentinel-1 Wave (WV) Mode acquisitions, generally acquired and delivered at a single polarization mode, thus with no default colorization scheme for quicklooks.

关键词： Deep learning Measurement visualization PSNR Statistical analysis Sentinel-1 Robustness

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SteamVis: A Tool for Collecting and Analyzing data of the Game Distribution Platform Steam

SteamVis: A Tool for Collecting and Analyzing Data of the Ga...

引用

ieee symposium on Computational Intelligence and Games, CIG

作者： Alexander Kainz Johanna Pirker Graz University of Technology Graz Austria Ludwig-Maximilians-Universität Germany

In recent years, the gaming industry has experienced rapid growth, further bolstered by the pandemic. Today, games are being utilized beyond entertainment, finding applications in education, health, training, and various industries. Understanding trends and analyzing games and their user base is therefore crucial for game developers, stakeholders, business analysts, and researchers alike. With over 60,000 games, Steam stands out as a major game distribution platform that collects valuable data about games and their users. To simplify the analysis of current trends in the gaming world, we have developed SteamVis: an analysis and visualization tool that efficiently crawls, displays, and analyzes game-related information from Steam. In this paper, we present the tool’s details, conducted case studies, and a qualitative evaluation by domain experts. SteamVis facilitates the study of games and game-related information by providing powerful analytical and visualization tools, enabling the discovery of important trends and insights.

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Understanding the Topography of the Gulf of Guinea Seabed Using GMT Scripting and GEBCO Gridded data

Understanding the Topography of the Gulf of Guinea Seabed Us...

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Geoscience and Remote Sensing symposium (M2GARSS), Mediterranean and Middle-East

作者： Swafiyudeen Bawa AbdulAzeez Aliyu Onotu Ebenezer Ayobami Akomolafe Usman Ibrahim Sa’i Department of Geomatics Ahmadu Bello University Nigeria

ISBN: (数字)9798350358582

ISBN: (纸本)9798350358599

The seabed topography of the Gulf of Guinea is still largely unexplored, despite being a vital and dynamic maritime region. By utilising GMT (Generic Mapping Tools) scripting in conjunction with GEBCO (General Bathymetric Chart of the Oceans) gridded data, this study revealed the complex topography of this region. A thorough analysis and visualisation of the bottom topography of the Gulf of Guinea are sought after through the combination of these potent instruments. The purpose of this study is to reveal the complex geological features, and comprehend changes in underwater topography. Through the use of GMT scripting, the study presents a strong approach for modifying and analysing GEBCO gridded data, offering a more thorough comprehension of the intricate and varied undersea environment in the Gulf of Guinea. The results have significant implications for a number of fields, such as resource management in this vital maritime region, environmental conservation, and marine exploration.

关键词： Oceans Instruments Geology Layout Geoscience and remote sensing data visualization Surfaces

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A Novel Automated Urban Building analysis Framework Based on GPT and SAM

A Novel Automated Urban Building Analysis Framework Based on...

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ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yuchao Sun Xianping Ma Yizhen Yan Man-On Pun Bo Huang School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China Research Institute for Smart Cities School of Architecture and Urban Planning Shenzhen University Shenzhen PR China Department of Geography The University of Hong Kong Hong Kong SAR China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Rapid urban development necessitates advanced methodologies for efficiently acquiring and analyzing detailed building information. This study proposes an automated framework, named Urban Street Buildings Scanner (USBS), which combines geospatial data and deep learning techniques to analyze street-level imagery, thereby providing valuable insights into building features. Specifically, we leverage the OSMnx library for geospatial data retrieval before exploiting coordinate sorting and interpolation techniques to extract information about buildings on both roadsides from street images. After that, Segment Anything Model (SAM) and GPT are utilized to segment each building in street-view images for analysis. YOLOv7 is used to further identify the windows of individual buildings and to analyze the buildings in a more detailed way. Meanwhile, GPT can also estimate the numbers of floors and the heights of the buildings, providing a more comprehensive street view analysis. The experimental results demonstrate the efficacy of the proposed framework in effectively extracting and analyzing building characteristics from street-level images. Visual representations and statistical data illustrate the successful application of the framework, providing valuable information for urban planning and development.

关键词： Deep learning Image segmentation Analytical models visualization Buildings Urban planning Windows

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LED CAVE - New Dimensions for large-Scale Immersive Installations

LED CAVE - New Dimensions for Large-Scale Immersive Installa...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ieee Conference on

作者： Elisabeth Mayer Thomas Odaker Daniel Kolb Simone Müller Dieter Kranzlmüller Leibniz Supercomputing Centre

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

Virtual reality is an established tool in many areas of application, including scientific visualisation. Hardware such as head-mounted displays and immersive installations can be utilised to analyse and display data. For the most part, these types of installations are based on projectors, making them complex and require a large space. We present the concept of a new CAVE system based on the latest generation of LED panels. We report our preliminary findings based on 6 months of operation. This paper focuses on the implementation, the comparison to a traditional projector-based CAVE and early user feedback. We discuss the potential future for large-scale immersive installation systems and the possibilities opened up by the advances in LED technology.

关键词： Three-dimensional displays Head-mounted displays Image color analysis Conferences Brightness data visualization Virtual reality

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An Efficient Metric-Based Approach for Static Use-After-Free Detection 20

An Efficient Metric-Based Approach for Static Use-After-Free...

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20th ieee Int symposium on Parallel and Distributed Processing with Applicat / 15th ieee Int Conf on Social Comp and Networking / 12th ieee Int Conf on Big data and Cloud Comp / 12th ieee Int Conf on Sustainable Comp and Commun

作者： Wei, Haolai Chen, Liwei Nie, Xiaofan Zhang, Zhijie Zhang, Yuantong Shi, Gang Univ Chinese Acad Sci Sch Cyber Secur Inst Informat Engn Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781665464970

Nowadays, attackers are increasingly using Use-After-Free(UAF) vulnerabilities to create threats against software security. Existing static approaches for UAF detection are capable of finding potential bugs in the large code base. In most cases, analysts perform manual inspections to verify whether the warnings detected by static analysis are real vulnerabilities. However, due to the complex constraints of constructing UAF vulnerability, it is very time and cost-intensive to screen all warnings. In fact, many warnings should be discarded before the manual inspection phase because they are almost impossible to get triggered in real-world, and it is often overlooked by current static analysis techniques. In this paper, we introduce a metric-based static analysis approach, named MAD, for efficiently identifying UAF vulnerabilities by removing redundant warnings. We design two sets of systematic metrics to drive MAD. First, we apply lightweight static analysis to locate potential UAF pairs and use Feature Metrics to gather their feature information into an evaluation pool. Then, we use Controllability Metrics to rank the evaluation pools and filter out the high ones as candidates for subsequent manual inspection. We have implemented MAD and evaluated it using Juliet Test Suite and a set of eight open-source C programs. MAD can locate all UAF bugs in Juliet Test Suite within a recognizable range, showing effectiveness and scalability by detecting 5 known CVEs with 1,286 KLOC in just 1.6 hours. Furthermore, we can achieve an average 75% reduction rate for reported warnings and save about half the time in locating UAF vulnerabilities during manual inspection.

关键词： use-after-free vulnerability static analysis program metric

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Demo: Explaining Time Series Interactively with ChronoProf

Demo: Explaining Time Series Interactively with ChronoProf

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ieee International symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Pablo Fernández Pérez Iñaki Bravo Anirudh Kamath Claudio Fiandrino Joerg Widmer IMDEA Networks Institute Spain Universidad Carlos III de Madrid Spain Univeristy of Utah USA

ISBN: (数字)9798331538323

ISBN: (纸本)9798331538330

Interpreting time series predictions from advanced Machine Learning and Deep Learning (ML/DL) models is challenging, as these models often function as black boxes, limiting their applicability in critical domains. To address this, we leverage CHRONOPROF, an Explainable AI (XAI) technique specifically designed for time series data, built upon the SHAP framework. CHRONOPROF improves interpretability by deriving virtual weights from SHAP values, offering a linearized representation of complex model decisions while preserving temporal coherence. However, CHRONOPROF’s complexity can pose challenges for non-expert users. To mitigate this, we developed an interactive dashboard that simplifies interpretation by retrieving stored data and SHAP values to compute and visualize virtual weights along with other representations that are derived from them. This user-friendly interface enables users to explore model behavior across different models and datasets. Ultimately, this innovation facilitates the adoption of CHRONOPROF and fosters trust in AI-driven network operations.

关键词： Wireless communication Deep learning Technological innovation Limiting Explainable AI Computational modeling Time series analysis data visualization Predictive models Complexity theory

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Delving into the Explainability of Prototype-Based CNN for Biological Cell analysis

Delving into the Explainability of Prototype-Based CNN for B...

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ieee International Conference on Image Processing

作者： Martin Blanchard Olivier Delézay Christophe Ducottet Damien Muselet CNRS Institut d Optique Graduate School Laboratoire Hubert Curien UMR 5516 Université Jean Monnet Saint-Etienne SAINT-ETIENNE France Laboratoire SAINBIOSE INSERM UMR 1059 Université Jean Monnet

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Deep learning for automated cell imaging analysis has become a tool of choice to process large amounts of data. But many of these methods lack explainability, slowing down their deployment for tasks such as diagnosis. We present a prototype-based framework to analyze structural changes which addresses the specific challenges of explainability in the context of cell imaging. Our method relies on classification between two distinct cell populations in a weakly supervised context where no label for individual cells is available. Our model extracts typical features from each population, representing intra-cellular structure, and provides an explanation on its classification decision by creating visualization of the local textures corresponding to the structures of interest. We show a real application where it effectively highlights a change in the organization of the actin content of the cells.

关键词： Deep learning visualization Neural networks Imaging Organizations Feature extraction Task analysis

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