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检索条件"任意字段=Conference on Visualization in Biomedical Computing"

共 897 条记录，以下是1-10 订阅

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Australian wildfire visualization 5th

Australian wildfire visualization

引用

5th International conference on Advances in AI for biomedical Instrumentation, Electronics and computing, ICABEC 2023

作者： Kaur, Manjinder Lal, Roop Pandey, Ankur Singh, Anurag Rajbhar, Dipak Chandigarh University India

ISBN: (纸本)9781032642987

In Australia, wildfires have grown to be a major problem, with disastrous effects on both human populations and ecosystems. Technology developments and improvements in data visualization have made it possible to track, comprehend, and communicate the effects of these fires in recent years. For instance, with an estimated 18.6 million hectares of land burned and 34 fatalities, the 2019-2020 Australian bushfire season was one of the worst in the nation’s history. Geographic information systems (GIS) and satellite imagery were heavily utilized during this time to track the fires and aid in emergency response. © 2024 The Author(s).

关键词： Satellite imagery

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Voxelization: Multi-target Optimization for biomedical Volume Rendering 16th

Voxelization: Multi-target Optimization for Biomedical Volum...

引用

Mediterranean conference on Medical and Biological Engineering and computing (MEDICON) and International conference on Medical and Biological Engineering (CMBEBIH)

作者： Denisova, Elena Manetti, Leonardo Bocchi, Leonardo Iadanza, Ernesto Univ Florence Dept Informat Engn Via S Marta 3 I-50139 Florence Italy Univ Siena Dept Med Biotechnol Via Banchi Sotto 55 I-53100 Siena Italy Epica Imaginalis Via Rodolfo Morandi 13-15 I-50019 Sesto Fiorentino Italy Eidolab Florence Italy

ISBN: (纸本)9783031490613;9783031490620

Almost all existing software for visualization of biomedical volumes provides three-dimensional (3D) rendering. The most common techniques for 3D rendering of volume data are maximum intensity projection (MIP) and direct volume rendering (DVR). Recently, rendering algorithms based on Monte-Carlo path tracing (MCPT) have also been considered. Depending on the algorithm, level of detail, volume size, and transfer function, rendering can be quite slow. In this paper, we present a simple and intuitive voxelization method for biomedical volume rendering optimization. The main advantage of the proposed method, besides the fast structure construction and traversal, is its straightforward application to MIP, DVR and MCPT rendering techniques (multi-target optimization). The same single structure (voxel grid) can be used for empty space skipping, optimized maximum intensity calculation and advanced Woodcock tracking. The performance improvement results suggest the use of the proposed method especially in cases where different rendering techniques are combined.

关键词： Rendering optimization Voxelization biomedical volumes visualization

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Beyond Time and Accuracy: Strategies in Visual Problem-Solving 25

Beyond Time and Accuracy: Strategies in Visual Problem-Solvi...

引用

2025 CHI conference on Human Factors in computing Systems, CHI 2025

作者： Mörth, Eric Kostic, Zona Gehlenborg, Nils Pfister, Hanspeter Beyer, Johanna Nobre, Carolina Department of Biomedical Informatics Harvard Medical School BostonMA United States SEAS Harvard University BostonMA United States Visual Computing Group Harvard University CambridgeMA United States University of Toronto TorontoON Canada

ISBN: (纸本)9798400713941

In this paper, we explore viewers' strategies in visual problem-solving tasks. We build on the traditional metrics of accuracy and time to better understand the learning that occurs as individuals interact with visualizations. We conducted an in-lab eye-tracking user study with 53 participants from diverse demographic backgrounds. Using questions from the visualization Literacy Assessment Test (VLAT), we examined participants' problem-solving strategies. We employed a mixed-methods approach capturing quantitative data on performance and gaze patterns, as well as qualitative data through think-alouds and sketches by participants as they reported on their problem-solving approach. Our analysis reveals not only the various cognitive strategies leading to correct answers but also the nature of mistakes and the conceptual misunderstandings that underlie them. This research contributes to the enhancement of visualization design guidelines by incorporating insights into the diverse strategies and cognitive processes employed by users. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Flow visualization

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Interactive visualization of biomedical Data

Interactive Visualization of BioMedical Data

引用

2024 IEEE International conference on Big Data, BigData 2024

作者： Islam, Mohammad Shafiqul Ibn Rafiq, Rahat Tusch, Guenter Grand Valley State University College of Computing MI United States

ISBN: (纸本)9798350362480

Public databases such as the NCBI Gene Expression Omnibus (GEO) house millions of experimental gene expression datasets invaluable for transcriptome meta-analysis, enabling researchers to identify genes, pathways, and biomarkers with enhanced accuracy. However, while these datasets offer great potential, finding relevant biological pathways in disease-related contexts remains challenging. To address this, we introduce a web-based visualization tool that utilizes PubMed abstracts to extract key biomedical entities, such as genes, pathways, diseases, species, and chemicals, and represent their relationships through an interactive graph. By leveraging PubMed's annotated MeSH terms, our tool allows users to explore the literature more intuitively, bypassing traditional dataset search limitations and focusing on published results. The tool transforms complex query outputs into a graph-based network, providing a visual interface for navigating connections between biological entities. This interactive approach helps researchers to analyze relationships within large-scale biomedical data and uncover potential pathways for drug repurposing and disease understanding. We discuss the tool's design using ***, its application in biomedical research, and the challenges faced when visualizing large datasets, offering new insights for improved data interpretation and exploration. © 2024 IEEE.

关键词： BioInformatics biomedical Data visualization PubMed

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An Intelligent visualization Method for Classic and Famous Prescriptions Based on Big Data 3

An Intelligent Visualization Method for Classic and Famous P...

引用

3rd IEEE International conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Yan, Xiangzhu Yu, Bing Changchun Humanities And Sciences College Center Of Well-being Training Changchun China

ISBN: (纸本)9798350318609

Relationships extraction using sMIL algorithm requires a set of classic and famous prescriptions data which is a data source that contains relational objects. The document data in this paper is the literature summary retrieved from the literature database such as PubMed. Therefore, in order to dig out the genetic data of the target syndrome of coronary heart in the literature as much as possible to improve the accuracy of excavation, data mining model where network decomposition and symptom combination are combined is proposed in this paper to mine the NEI gene data of the literature related to target syndrome in coronary heart disease with the help of a large number of previous literature research and previous research of the research group. Then final conclusion will be reached through comprehensively considering the mining results of these two methods. Data source of this paper is a large number of biomedical literatures containing classic and famous prescriptions in some disease. © 2024 IEEE.

关键词： visualization

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Low-Power Approximate computing-based VLSI Architecture for biomedical Signal Processing

Low-Power Approximate Computing-based VLSI Architecture for ...

引用

2025 International conference on Visual Analytics and Data visualization, ICVADV 2025

作者： Sathiya, A. Prabhavathy, B. Balasupramani, A. Amutha, A. Kavitha, L. Saravanakumar, R. Department of Electronics and Communication Engineering Karur Thalavapalayam639113 India E.G.S. Pillay Engineering College Department of Electronics and Communication Engineering Tamil Nadu Nagapattinam611002 India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chennai600124 India Dhanalakshmi Srinivasan College of Engineering Department of Electrical and Electronics Engineering Coimbatore641105 India Thalavapalayam639113 India Velalar College of Engineering and Technology Department of Biomedical Engineering Erode638012 India

ISBN: (纸本)9798331521394

The analysis of ECG, EEG and EMG signals requires biomedical signal processing which needs low-power high-performance VLSI architectures to achieve real-time processing. This paper introduces an optimized VLSI architecture which enables biomedical signal processing through energy-efficient digital filtering along with feature extraction as well as pipeline-based clock gating. The proposed architecture combines a distributed arithmetic FIR filter without multipliers to reduce complexity and it integrates a low-power feature extraction system that detects ECG R-peaks and EEG spectral energy and EMG muscle activity effectively. The system uses dynamic clock gating together with power gating to cut down both dynamic power usage and leakage power. The experimental findings show that the developed VLSI architecture both reduces power usage through low hardware complexity while keeping high accuracy levels for biomedical signal processing. The system delivers its performance measurement through power dissipation and area efficiency along with processing speed which enables its use in wearable and implantable medical devices. © 2025 IEEE.

关键词： Medical image processing

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AR²T: Advanced Realistic Rendering Technique for biomedical Volumes 26th

AR<SUP>2</SUP>T: Advanced Realistic Rendering Technique for ...

引用

26th International conference on Medical Image computing and Computer-Assisted Intervention (MICCAI)

作者： Denisova, Elena Manetti, Leonardo Bocchi, Leonardo Iadanza, Ernesto Univ Florence Dept Informat Engn I-50139 Florence Italy Univ Siena Dept Med Biotechnol I-53100 Siena Italy Imaginalis Srl I-50019 Sesto Fiorentino Italy Eidolab Florence Italy

ISBN: (纸本)9783031439865;9783031439872

Three-dimensional (3D) rendering of biomedical volumes can be used to illustrate the diagnosis to patients, train inexperienced clinicians, or facilitate surgery planning for experts. The most realistic visualization can be achieved by theMonte-Carlo path tracing (MCPT) rendering technique which is based on the physical transport of light. However, this technique applied to biomedical volumes has received relatively little attention, because, naively implemented, it does not allow to interact with the data. In this paper, we present our application of MCPT to the biomedical volume rendering-Advanced Realistic Rendering Technique (AR(2)T), in an attempt to achieve more realism and increase the level of detail in data representation. The main result of our research is a practical framework that includes different visualization techniques: iso-surface rendering, direct volume rendering (DVR) combined with local and global illumination, maximum intensity projection (MIP), and AR(2)T. The framework allows interaction with the data in high quality for the deterministic algorithms, and in low quality for the stochastic AR(2)T. A high-quality AR(2)T image can be generated on user request;the quality improves in real-time, and the process is stopped automatically on the algorithm convergence, or by user, when the desired quality is achieved. The framework enables direct comparison of different rendering algorithms, i.e., utilizing the same view/light position and transfer functions. It therefore can be used by medical experts for immediate one-to-one visual comparison between different data representations in order to collect feedback about the usefulness of the realistic 3D visualization in clinical environment.

关键词： Monte-Carlo Path Tracing Realistic Rendering biomedical Volumes visualization

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GUI Toolkit for Pulmonologists: Age-Gender Specific Lung Sound Analysis and Disease Identification Using Sequence Modeling 10

GUI Toolkit for Pulmonologists: Age-Gender Specific Lung Sou...

引用

10th International conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Amose, John Manimegalai, P. Pavithra, S. Susmitha, B. Ruth, S. Priyanga, S. Karunya Institute of Technology and Sciences Department of Biomedical Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Biomedical Engineering Coimbatore India

ISBN: (纸本)9798350384369

The objective diagnosis of respiratory diseases, by observing pathological sounds is a promising area of research in respiratory health. A GUI-based toolkit to aid a pulmonologist in decision-making is presented in this paper. Age and Gender-specificmachine learning models are trained and analyzed in this work for the classification of wheeze and crackle. Sequence models like LSTM, GRU, and Transformer were implemented, demonstrating high accuracy in classifying intricate lung sound features for diagnosing various respiratory diseases. In this work, MATLAB is used to enable user-friendly visualization, featuring filtered spectrograms and time- domain lung sound signals. In experimental evaluations, age and gender-specific models (child, adult, female) outperformed general models in the classification of wheeze and crackle. GRU and LSTM models achieved 69% accuracy, while the Transformer model outperformed with 750%. © 2024 IEEE.

关键词： Spectrographs

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2024 13th Mediterranean conference on Embedded computing, MECO 2024

2024 13th Mediterranean Conference on Embedded Computing, ME...

引用

13th Mediterranean conference on Embedded computing, MECO 2024

ISBN: (纸本)9798350387568

The proceedings contain 114 papers. The topics discussed include: application of artificial neural networks for processing some biomedical data;distinguishing between AI images and real images with hybrid image classification methods;securing Durres Port's digital transformation: cybersecurity strategy for maritime industry;linguistic encryption for underwater communication;a toolset for blood pressure visualization and measurement in time, frequency and time-frequency domains;using a shape from polarization to determine the 3D surface of objects with thermal radiation;on the influence of cell libraries and other parameters to SCA resistance of crypto IP cores;integration of PXROS-HR with micro-ROS in robotic systems;and traffic-aware video streaming topology reconfiguration for smart city applications.

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7th Joint International Workshop on computing and visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2018, and the 3rd International Workshop on Large-Scale Annotation of biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018

7th Joint International Workshop on Computing and Visualizat...

引用

7th Joint International Workshop on computing and visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2018, and the 3rd International Workshop on Large-Scale Annotation of biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018

ISBN: (纸本)9783030013639

The proceedings contain 21 papers. The special focus in this conference is on computing and visualization for Intravascular Imaging and Computer Assisted Stenting. The topics include: Deep learning retinal vessel segmentation from a single annotated example: An application of cyclic generative adversarial neural networks;an efficient and comprehensive labeling tool for large-scale annotation of fundus images;crowd disagreement about medical images is informative;imperfect segmentation labels: How much do they matter?;crowdsourcing annotation of surgical instruments in videos of cataract surgery;Four-dimensional ASL MR angiography phantoms with noise learned by neural styling;Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans;capsule networks against medical imaging data challenges;fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images;weakly-supervised learning for tool localization in laparoscopic videos;Automated quantification of blood flow velocity from time-resolved CT angiography;Radiology objects in COntext (ROCO): A multimodal image dataset;Improving out-of-sample prediction of quality of MRIQC;multiple device segmentation for fluoroscopic imaging using multi-task learning;segmentation of the aorta using active contours with histogram-based descriptors;layer separation in X-ray angiograms for vessel enhancement with fully convolutional network;Generation of a HER2 breast cancer gold-standard using supervised learning from multiple experts;Deep learning-based detection and segmentation for bvs struts in IVOCT images;towards automatic measurement of type B aortic dissection parameters: Methods, applications and perspective;Prediction of FFR from IVUS images using machine learning.

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