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

共 893 条记录，以下是61-70 订阅

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

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

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Distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE International conference on

作者： Xiangzhu Yan Bing Yu Center of Well-being Training Changchun Humanities and Sciences College Changchun China

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

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.

关键词： Heart Technological innovation Databases Soft sensors Big Data Genetics Excavation

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Comparison of Machine Learning Models for Injury Prediction in Athletes

Comparison of Machine Learning Models for Injury Prediction ...

引用

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

作者： Murugan, Suganiya Kumar, S. Pradeep Kalaiarasi, G. Saritha, B. Rubini, B. Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Vels Institute of Science Technology and Advanced Studies Department of Eee Tamil Nadu Chennai India Vignan Foundation for Science Technology and Research Department of Acse Andhrapradesh India Erode Sengunthar Engineering College Thudupathi Erode Department of Biomedical Engineering Tamil Nadu India Vels Institute of Science Technology and Advanced Studies Department of Eee TamilNadu Chennai India

ISBN: (纸本)9798331521394

Running is a favoured exercise for millions, but it carries a risk of musculoskeletal injuries, particularly to the lower extremities. Identifying and predicting potential injuries is crucial for prevention. This study explores the use of machine learning techniques to predict injuries among runners, leveraging data on demographics, running habits, health conditions, and previous injuries. Using models like Random Forest, K-Nearest Neighbours, Decision Trees, and Logistic Regression, the study examines the relevance of various attributes and assesses the predictive power of these models. Remarkably, our model achieved an impressive accuracy of over 99%, setting a new benchmark for predictive precision in injury prevention. visualizations provide insight into attribute interactions and their influence on injury risk, facilitating the development of targeted prevention strategies. © 2025 IEEE.

关键词： Logistic regression

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Progressive GAN Framework for Realistic Chest X-Ray Synthesis and Data Augmentation

Progressive GAN Framework for Realistic Chest X-Ray Synthesi...

引用

Mobile computing and Sustainable Informatics (ICMCSI), International conference on

作者： Kiran Kumar Maguluri Venkata Krishna Azith Teja Ganti Zakera Yasmeen Chandrashekar Pandugula Cigna Plano Texas USA Microsoft Corporation Charlotte NC USA Microsoft Columbus Ohio USA Lowes Inc NC USA

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

The scarcity of large-scale labeled medical imaging datasets presents a significant bottleneck for advancing diag-nostic model performance in radiology. This study introduces a progressive Generative Adversarial Network (GAN) framework for synthesizing high-quality, anatomically consistent chest x-ray images. The proposed model leverages a generator with progressive upsampling and residual connections to preserve fine-grained anatomical details and a PatchGAN-based discriminator to ensure local and global realism. A comprehensive preprocessing pipeline, including contrast enhancement, morphological operations, Fourier analysis, and data augmentation, was employed to prepare high-quality input data. Quantitative evaluation metrics such as Frechet Inception Distance (FID) and Structural Similarity Index (SSIM) demonstrated the model's effectiveness, achieving low FID scores and high SSIM values, indicative of the synthetic images' similarity to real chest X-rays. Qualitative analyses further validated the generated images' clinical relevance and visual fidelity, highlighting their potential for augmenting medical imaging datasets and supporting downstream diagnostic tasks.

关键词： visualization Pipelines Radiology Generative adversarial networks Data augmentation X-ray imaging Mobile computing biomedical imaging Synthetic data Morphological operations

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6th Joint International Workshops on computing and visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2017 and 2nd International Workshop on Large-Scale Annotation of biomedical Data and Expert Label Synthesis, LABELS 2017 held in Conjunction with 20th International conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2017

6th Joint International Workshops on Computing and Visualiza...

引用

6th Joint International Workshops on computing and visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2017 and 2nd International Workshop on Large-Scale Annotation of biomedical Data and Expert Label Synthesis, LABELS 2017 held in Conjunction with 20th International conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2017

ISBN: (纸本)9783319675336

The proceedings contain 17 papers. The special focus in this conference is on computing and visualization for Intravascular Imaging and Computer Assisted Stenting. The topics include: Robust detection of circles in the vessel contours and application to local probability density estimation;intra-coronary stent localization in intravascular ultrasound sequences, a preliminary study;robust automatic graph-based skeletonization of hepatic vascular trees;DCNN-based automatic segmentation and quantification of aortic thrombus volume: influence of the training approach;vascular segmentation in TOF MRA images of the brain using a deep convolutional neural network;automatic vessel-of-intervention dynamic detection in PCI procedures;exploring the similarity of medical imaging classification problems;real data augmentation for medical image classification;detecting and classifying nuclei on a budget;towards an efficient way of building annotated medical image collections for big data studies;expected exponential loss for gaze-based video and volume ground truth annotation;crowdsourced emphysema assessment;a web-based platform for distributed annotation of computerized tomography scans;training deep convolutional neural networks with active learning for exudate classification in eye fundus images and uncertainty driven multi-loss fully convolutional networks for histopathology.

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Lightpath simulation in optical systems based on matrix optics method

Lightpath simulation in optical systems based on matrix opti...

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2020 International conference on computing and Data Science, CDS 2020

作者： Xu, Haoyu Department of Biomedical Engineering Peking University Beijing China

ISBN: (纸本)9781728171067

Optical simulation often plays an essential role in the field of imaging. The visualization of lightpath and setup especially worth considering for it directly illustrates the property of the optical system. However, commercial optical simulation software is usually massive in volume, costs high, and requires training for operation. In this paper, a simulation method will be introduced for the visualization of the optical lightpath and the setup. In this work, an algorithm in Python/Matlab environment based on matrix optics, which is an efficient approach for describing both geometrical and wave optical lightpath as well. Conducted several simulations, the algorithm yields accurate results, demonstrating the practical usage of the method, while the friendly environment promising a further improvement of the algorithm. © 2020 IEEE

关键词： Optical systems

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A Systematic Review of Slam Techniques for AMRs in Warehouse Environments

A Systematic Review of Slam Techniques for AMRs in Warehouse...

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Cognitive computing in Engineering, Communications, Sciences and biomedical Health Informatics (IC3ECSBHI), International conference on

作者： Kante Aditya Prasad Harsh Kumar Usha Tiwari Deptt. Of Electrical Electronics & Communication Engineering Sharda University Greater Noida India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

The tremendous growth of the logistics sector has been driven by just in time principles, mass customization, omni channel distribution and a growing world population. These changes have led to the need for more flexibility and faster speed in supply chain operations encouraging the development of smart warehouse technologies. In this review article, we discuss the latest advance in warehouse automation with the emphasis on the importance of SLAM (Simultaneous localization and mapping) in improving the efficiency and scale of autonomous warehouse robots. Effective warehouse movement would rely upon autonomous systems that can map new areas while determining their pose, and SLAM provides this capacity. This work investigates various SLAM implementations based on Visual SLAM (VSLAM), LIDAR based SLAM, and Multi sensor SLAM, and describes their merits in different warehouse scenarios. We cover challenges and recent breakthroughs in SLAM, including its deployment in a number of settings, task completion autonomously while reacting to changes in environment, and its utility for performing complicated exploratory tasks without prior knowledge or human intervention. Additionally, the importance of order picking optimization is stressed, admitting it as one of the most important warehouse processes. This analysis discusses how the SLAM (Simultaneous Localization and Mapping) technologies are allied with AGVs (Autonomous Ground Vehicles) and AMRs (Autonomous Mobile Robots) to give precise navigation, suitable order picking and overall improvement of warehouse operations.

关键词： visualization Simultaneous localization and mapping Laser radar Automation Accuracy Navigation Mobile robots Vehicle dynamics Optimization Autonomous robots

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Visual Understanding and Navigation for the Visually Impaired Using Image Captioning

Visual Understanding and Navigation for the Visually Impaire...

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Cognitive computing in Engineering, Communications, Sciences and biomedical Health Informatics (IC3ECSBHI), International conference on

作者： Jitesh Uikey Ishan Undre Urvish Vasani Aman Shaikh Riddhi Mirajkar Jayashree Bagade Department of Information Technology Vishwakarma Institute Of Information Technology Pune India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Visually impaired people face problems with independent navigation due to limited visual information. Facing significant challenges, they often travel with an assistant or their relative. This project introduces an approach, increasing the independence of a blind user by developing new software solutions. The proposed system employs DenseNet201 for feature extraction and Long Short-Term Memory (LSTM) networks for generating accurate, context-aware captions. These captions are converted into real-time auditory descriptions using the gTTS library, enabling users to interpret and navigate their environment confidently. Evaluated on the Flickr8k dataset, the system achieved a BLEU score of 0.721, demonstrating its ability to generate high-quality captions. The system's architecture is designed to balance accuracy, efficiency, and user accessibility. Additionally, the system incorporates a modular architecture optimized for computational efficiency and scalability. Future work includes exploring wearable technology for continuous real-time feedback, integrating advanced natural language processing (NLP) models for richer contextual understanding, and enhancing its applicability in complex indoor and outdoor environments. This approach represents a significant step toward empowering visually impaired individuals with improved mobility and environmental awareness.

关键词： Deep learning visualization Navigation Computer architecture Feature extraction Natural language processing Real-time systems Convolutional neural networks Wearable devices Long short term memory

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A Recent Advancements in Multi-Modal Medical Image Fusion Techniques using SWT, NSST, NSCT, and CNN

A Recent Advancements in Multi-Modal Medical Image Fusion Te...

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Cognitive computing in Engineering, Communications, Sciences and biomedical Health Informatics (IC3ECSBHI), International conference on

作者： Shailesh Bhosekar Prabhishek Singh Deepak Garg School of Computer Science and Artificial Intelligence SR University Warangal Telangana India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Image fusion is a method used in image processing to provide a more complete representation by amalgamating features and data from many images. Multimodal medical image fusion involves the integration of medical images from many imaging modalities, including computed tomography (CT) scans, positron emission tomography (PET), and magnetic resonance imaging (MRI), into one single dataset. This integration enhances the visualisation of anatomical structures and clinical situations, hence improving diagnostic accuracy by leveraging the strengths of each medium. This study employs MRI, CT, and PET scans as experimental modalities. This review aims to compare various multi modal medical image approach based on Stationary Wavelet Transform (SWT), Non-Subsampled Shearlet Transform (NSST), Convolutional Neural Network (CNN) and NonSubsampled Contourlet Transform (NSCT). This study examines the latest conventional and non-conventional research conducted within these disciplines. It further evaluates these approaches according to diverse image quality metrics and many quantitative assessments. According to this comparison, CNN-based fusion demonstrates superior results, as the overall visual and parametric quality of the fusion outcomes surpasses that of the other approaches evaluated.

关键词： Image quality Wavelet transforms visualization Reviews Magnetic resonance imaging Computed tomography Convolutional neural networks Positron emission tomography Medical diagnostic imaging Image fusion

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Implementation of Pneumoconiosis Detection xAI in Chest X-ray Images

Implementation of Pneumoconiosis Detection xAI in Chest X-ra...

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International Symposium on Soft computing and Intelligent Systems (SCIS)

作者： Yoshua K Nomura Yuki Hyohdoh Keita Mitani Yutaka Hatakevama Center of Medical Information Science Kochi Medical School Kochi University Nankoku Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

This study aims to visualize pneumoconiosis lesions from chest X-ray images using an improved Grad-CAM method and evaluate its performance. Pneumoconiosis is a disease that causes fibrotic changes in the lungs due to the inhalation of dust, and there is no cure. Diagnosis is usually performed by interpreting chest X-ray images, which requires skilled radiologists who are in short supply. Recently, diagnostic support using deep learning has garnered attention, and visualization methods such as Grad-CAM are used to explain these supports. However, these methods have not been sufficiently validated and have issues such as low-resolution visualizations. We constructed a pneumoconiosis detection model using the VGG16 model and applied GradCAM, GradCAM++, ScoreCAM, and LayerCAM methods to visualize the lesions. The VGG16 model showed high performance with both sensitivity and specificity of 0.94 in detecting pneumoconiosis. While all visualization methods exhibited high specificity, sensitivity was generally low, with LayerCAM being the lowest. Although ScoreCAM showed potential usefulness, overall visualization performance was low. It was suggested that LayerCAM is unsuitable for diffuse lung diseases like pneumoconiosis. Additionally, the sensitivity might have decreased due to narrowing down the Activation Map values to 0.7 or higher. Future research requires precise annotation and expansion of datasets. The study indicated the usefulness of ScoreCAM as a method for detecting and visualizing pneumoconiosis from chest X-ray images, although improvements in overall visualization performance are needed. LayerCAM may not be suitable for visualizing pneumoconiosis.

关键词： Location awareness Deep learning visualization Sensitivity Pulmonary diseases Lungs Sensitivity and specificity Lesions X-ray imaging biomedical imaging

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Enhancing Perceptual Quality of Images using Deep Residual U-Net and PatchGAN Discriminator

Enhancing Perceptual Quality of Images using Deep Residual U...

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International conference on New Trends in computing Sciences (ICTCS)

作者： Satvik Raghav Sathwik Narkedimilli Prasanth Ayitapu Raja Karthikeya Lalitha Sreeram Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science Engineering Indian Institute of Information Technology (IIIT) Dharwad India

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

This paper presents a novel approach for perceptually aware image enhancement utilizing a deep residual U-Net generator combined with a Patch-based Generative Adversarial Network (PatchGAN) discriminator. Our method incorporates a perceptual loss function based on a pre-trained VGG16 network, ensuring that enhanced images exhibit improved color sharpness, clarity, and retention of critical high-level features. Experiments show improved structural similarity, with SSIM of 0.9270 and FSIM of 0.9998. These results indicate both perceptual and structural enhancement, while the model efficiently generates high-quality images with minimal training. Our method consistently outperforms conventional approaches in visual appeal and computational efficiency, making it relevant for real-world applications such as radiology, self-driving vehicles, and low-light photography. Our implementation is open-sourced and available here: https://***/bp8zsl.

关键词： Training visualization Technological innovation Smart cities Service robots Computational modeling Surveillance Image enhancement biomedical imaging Sports

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