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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181-190 订阅

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Virtual Reality and visualization of 3D Reconstructed Medical Imaging: Learning Variations Within Detailed Human Anatomies

Virtual Reality and Visualization of 3D Reconstructed Medica...

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Future Technologies conference, FTC 2020

作者： Gaasedelen, Erik N. Deakyne, Alex J. Mattson, Alexander R. Mattison, Lars M. Holm, Mikayle A. Sanchez, Jorge D. Zhingre Schmidt, Megan M. Bateman, Michael G. Iles, Tinen L. Iaizzo, Paul A. Departments of Bioinformatics and Computational Biology Surgery and Biomedical Engineering University of Minnesota Minneapolis United States

ISBN: (纸本)9783030630911

The emerging field of virtual reality has many promising new applications for the medical sciences. For example, by converting magnetic resonance and tomography-based images into 3D models, users can visually inspect individualized anatomic reconstructions at clinically useful high resolutions. Yet, adequate development of these tools will require a wide breadth of associated expertise to take advantage of current video game technologies while maintaining relevance for clinical use. Our laboratory has begun to implement such system approaches for the exploration of hearts, cadaveric specimens, and medical device-tissue interactions. We have created hundreds of anatomical scenes that were developed using physician feedback from conferences worldwide. We demonstrate several aspects of the potential applicability of virtual reality to serve both clinical science and education, and additionally discuss future prospects. © 2021, Springer Nature Switzerland AG.

关键词： Virtual reality

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Explainable COVID-19 Three Classes Severity Classification Using Chest X-Ray Images

Explainable COVID-19 Three Classes Severity Classification U...

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IEEE EMBS conference on biomedical Engineering and Sciences (IECBES)

作者： P. L. Thon J. C. M. Than R. M. Kassim A. Yunus N. M. Noor P. Then Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Sarawak Malaysia Kuala Lumpur Hospital Kuala Lumpur Malaysia Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur Campus Kuala Lumpur Malaysia

COVID-19 has been raging for almost three years ever since its first outbreak. It is without a doubt that it is a common human goal to end the pandemic and how it was before it started. Many efforts have been made to work toward this goal. In computer vision, works have been done to aid medical professionals into faster and more effective procedures when dealing with the disease. For example, disease diagnosis and severity prediction using chest imaging. At the same time, vision transformer is introduced and quickly stormed its way into one of the best deep learning models ever developed due to its ability to achieve good performance while being resources friendly. In this study, we investigated the performance of ViT on COVID19 severity classification using an open-source CXR images dataset. We applied different augmentation and transformation techniques to the dataset to see ViT’s ability to learn the features of the different severity levels of the disease. It is concluded that training ViT using the horizontally flipped images added to the original dataset gives the best overall accuracy of 0.862. To achieve explainability, we have also applied Grad-CAM to the best performing model to make sure it is looking at relevant region of the CXR image upon predicting the class label.

关键词： COVID-19 Training Deep learning visualization Biological system modeling Pipelines Medical diagnosis

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Multiple Ways for Medical Data visualization Using 3D Slicer

Multiple Ways for Medical Data Visualization Using 3D Slicer

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2020 International conference on Computational Science and Computational Intelligence, CSCI 2020

作者： Bahkali, Ismail Mohammed Semwal, Sudhanshu Kumar University of Colorado Colorado Springs Department of Computer Science United States

ISBN: (纸本)9781728176246

Computers can process large amounts of data. Medical practitioners can deliver better services and provide more accurate diagnoses and treatment regimens to patients. This document described how 3D Slicer allows Command Line Interface (CLI), Python, Jupyter, and MATLAB in software to process medial data. 3D Slicer has become useful software worldwide since 1997, especially in the medical field for preoperative visualization and analysis. Today, 3D Slicer is supported by The National Alliance for Medical Imaging computing (NA-MIC), Neuroimaging Analysis Center (NAC), biomedical Informatics Research Network (BIRN), The National Center for Image-Guided Therapy (NCIGT), The Harvard Clinical and Translational Science Center (CTSC), and the Slicer Community worldwide as a platform to develop new ideas. In this paper, we demonstrate our knowledge in using the 3D Slicer software. © 2020 IEEE.

关键词： Python

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10th International conference on Health Informatics and Medical Systems, HIMS 2024 and 10th International conference on biomedical Engineering, BIOENG 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied computing, CSCE 2024

10th International Conference on Health Informatics and Medi...

引用

10th International conference on Health Informatics and Medical Systems, HIMS 2024 and 10th International conference on biomedical Engineering, BIOENG 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied computing, CSCE 2024

ISBN: (纸本)9783031859076

The proceedings contain 44 papers. The special focus in this conference is on Health Informatics and Medical Systems. The topics include: White Blood Cell Nuclei Detection and Segmentation on Ratio Channels G/B, B/G, and B/R for FPGA Implementation;mobiVitalsConnect: A Comprehensive Mobile Healthcare System for Real-Time Patient Monitoring and Data visualization;internet-of-Things Chair and the 30 s Chair Stand Test;an Overview of Smart Wheelchair Technologies;Using EEG Signal for Smart Health: An Overview;Classification of Heart Rate Variability (HRV) Based on Attention Mechanism of Transformer Model;advancing Fall Detection and Prevention: Integrating Wearable Devices and a Camera-Based Analysis;A Performance Comparison Between Two Speech-To-ASL-Gesture-Projection Translation Implementations;facial Tilt Detection and Correction in Panoramic X-Rays Using Depth Maps from 2D Stereo Images: A Novel Approach to Minimize Positioning Errors in Dental Imaging;EEGNet-Vision Transformer: Ensemble Model with Pseudo Labeling for Epilepsy Patients Seizure Prediction from EEG;Off-line CNN Based Finger Number Gesture Recognition System Using Raspberry Pi 4;comparing Models for Sentiment Analysis of Tweets in Response to Public Health Announcements During the Pandemic;modeling the Dynamics of Infectious Diseases in a College Campus: A Case Study of the 2016 Harvard Mumps Outbreak;a Respite Work Exchange Service Mechanism Based on Blockchain Technology;explainable Machine Learning-Based Alzheimer’s Disease Prediction;Transformer-Based Encoder Method for Classification of Heart Rate Variability (HRV);Electronic Health Record Summarization via LLM-Constructed Knowledge Graphs;perspective of Family and Staff About Using Robots;ethical Perspectives Into the Utilization of Health Informatics for Cancer Care;gender Differences in Working Memory – A Holistic Review;Automatic Wound Detection System for Multi-ethnic Populations Using YOLO;Prediction of the Effectiveness of rTMS for Depress

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17th Smoky Mountains Computational Sciences and Engineering conference, SMC 2020

17th Smoky Mountains Computational Sciences and Engineering ...

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17th Smoky Mountains Computational Sciences and Engineering conference, SMC 2020

ISBN: (纸本)9783030633929

The proceedings contain 37 papers. The special focus in this conference is on Smoky Mountains Computational Sciences and Engineering. The topics include: visualization as a service for scientific data;Performance improvements on SNS and HFIR instrument data reduction workflows using Mantid;software framework for federated science instruments;automated integration of continental-scale observations in near-real time for simulation and analysis of biosphere-atmosphere interactions;Toward real-time analysis of synchrotron micro-tomography data: Accelerating experimental workflows with AI and HPC;unsupervised anomaly detection in daily wan traffic patterns;from smart homes to smart laboratories: Connected instruments for materials science;machine learning for the complex, multi-scale datasets in fusion energy;data federation challenges in remote near-real-time fusion experiment data processing;large-scale neural solvers for partial differential equations;software defined infrastructure for operational numerical weather prediction;openshmem i/o extensions for fine-grained access to persistent memory storage;Distributed transaction and self-healing system of DAOS;Truly heterogeneous HPC: Co-design to achieve what science needs from HPC;performance evaluation of python based data analytics frameworks in summit: Early experiences;Navigating the road to successfully manage a large-scale research and development project: The exascale computing project (ECP) experience;memory vs. Storage software and hardware: The shifting landscape;ALAMO: Autonomous lightweight allocation, management, and optimization;smoky mountain data challenge 2020: An open call to solve data problems in the areas of neutron science, material science, urban modeling and dynamics, geophysics, and biomedical informatics;examining and presenting cycles in temperature logs from the Vulcan diffractometer;probability flow for classifying crystallographic space groups.

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Drug-drug interaction extraction from biomedical texts based on multi-attention mechanism 21

Drug-drug interaction extraction from biomedical texts based...

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8th International conference on Bioinformatics Research and Applications (ICBRA)

作者： Wu, Chengkun Wang, Wei Yang, Xi Yang, Canqun Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Changsha 410073 Peoples R China Natl Supercomp Ctr Tianjin Tianjin 300457 Peoples R China

ISBN: (纸本)9781450384261

With the recent revival of interest in deep neural networks, many studies have focused on exploring deep learning methods for automatic DDIs (Drug-drug Interactions) extraction. However, due to the "black box" nature of deep neural networks, the researches on discussing the interpretability and reliability of the output from the models still fall short. Considering the interpretability of the output, we propose a model based on mechanism for DDIs extraction. Our model is able to compute the importance scores in the neural network, which can be utilized to measure the contributions of different words while the model makes decision. Besides, a key feature of our model is that we design a classified tag word, named as [CLS], to learn the global information for DDI classification. Our model consists of three layers, including Bi-LSTM layer, attention layer and dense Layer. The Bi-LSTM layer uses a bi-directional long short term memory network to learn embeddings for each word, also for the [CLS] tag. We equip the attention layer with a multi-head attention mechanism to learn different contributions of each work for the classified tag word. In the dense layer, instead of performing pooling to capture global information across the whole sentence, we combine two [CLS] tags produced by the Bi-LSTM layer and the attention layer, respectively. At last, we use a Softmax function for classification. The experimental results have shown that our model has competitive advantage in extract the relation between the two candidate drugs in one instance. By visualizing the attention weights with the corresponding instance, it offers an intuitive way to understand the basis of the classification results. Moreover, we use the [CLS] tag to learn the global information instead of performing pooling in the dense layer, which can reduce the training time and also improves performance.

关键词： DDI extraction attention mechanism deep neural networks result visualization

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LogTrans: Providing Efficient Local-Global Fusion with Transformer and CNN Parallel Network for biomedical Image Segmentation

LogTrans: Providing Efficient Local-Global Fusion with Trans...

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IEEE International conference on High Performance computing and Communications (HPCC)

作者： Xingqing Nie Xiaogen Zhou Zhiqiang Li Luoyan Wang Xingtao Lin Tong Tong College of Physics and Information Engineering Fuzhou University Fuzhou China Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology Fuzhou University Fuzhou China Imperial Vision Technology Fuzhou China

Accurate biomedical image segmentation is a prerequisite for excellent computer-aided diagnosis (CAD) systems. A series of researches have shown that convolutional neural networks (CNNs) have made impressive progress in segmentation tasks. Nevertheless, owing to the finite receptive field of CNN-based algorithms, such networks are focused too much on local area features rather than global context. While the Transformer architecture can encode global dependencies information through the self-attention mechanism, this mechanism typically ignores the local pixel-level structural information within each divided patch. Therefore, a better solution is still needed for how to integrate CNN architecture with Transformer architecture efficiently. In this essay, we propose an originative parallel segmentation algorithm called LogTrans. First, in the encoder path, the local details and global contour dependencies on the entire image are captured by the CNN branch and the Transformer branch, respectively. Then these two branches complement each other by a novel separate-combiner (SeCo) module, leading to better fused features. Moreover, we attempt to further enhance the segmentation properties by using a residual stackable dilated (ReS $D$ ) block, which applies residual shortcut connections to resolve dimension alterations in the target region and stacks dilated convolutions to capture more spatial information. The proposed LogTrans framework was evaluated on two biomedical datasets, including ISIC-2017 and UITNS-2022 datasets. Collectively, multiple results have indicated that our LogTrans performs superior with other state-of-the-art architectures in both visual comparison and quantitative appraisal.

关键词： Image segmentation visualization Fuses Transformers Decoding Image restoration Convolutional neural networks

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IoT Based Real Time Healthcare Monitoring System and visualization

IoT Based Real Time Healthcare Monitoring System and Visuali...

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2020 International conference on System, Computation, Automation and Networking, ICSCAN 2020

作者： Mathivanan, P. Mohanapriya, N. Nivedha, K. Priyadharshini, R. Swetha, G. Manakula Vinayagar Institute of Technology Department of Information Technology Pondicherry India

ISBN: (数字)9781728162027

ISBN: (纸本)9781728162027

The health and welfare sector are essential to human society and there are primary area to get major advantages of IoT technologies. A number of healthcare related data collected from different area unit are connected IoT network in order to observe the everyday activities of the patients. Recently, researches are coming up with new medical devices to monitor the medications of the patient in order to facilitate a better future life for them. We have a tendency to area unit observance numerous parameters of the patient exploitation the web of Things. Within the patient observation system supported the web of things, the parameter period time to the patient's health area unit sent the cloud mistreatment web property. These parameters area unit sent to a foreign web location so user will read these details from anyplace within the world. There's a significant distinction between SMS primarily relate patient health examination and IoT related patient observation method. In Internet of things primarily related methods, features of the patient health may be pot by many people. The operation beyond this can be that the particulars must be watched by visiting a website or address and mobile app although but in GSM based mostly patient observation, the health parameters area unit sent exploitation GSM via SMS. © 2020 IEEE.

关键词： Medical services Sensors Temperature sensors Microcontrollers Cloud computing Monitoring biomedical monitoring biomedical monitoring physician services units of area Microcontrollers Temperature Sensor Device Component Cloud computing Watchful Waiting Internet of things Sensors Mobile Applications health visualization Medicine Global mobile communication-systems wireless phone

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Comparative evaluations of cnn based networks for skin lesion classification 14

Comparative evaluations of cnn based networks for skin lesio...

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14th IADIS International conference Computer Graphics, visualization, Computer Vision and Image Processing 2020, CGVCVIP 2020 and 5th IADIS International conference Big Data Analytics, Data Mining and Computational Intelligence 2020, BigDaCI 2020 and 9th IADIS International conference Theory and Practice in Modern computing 2020, TPMC 2020, Part of the 14th Multi conference on Computer Science and Information Systems, MCCSIS 2020

作者： Goceri, Evgin Karakas, Ayse Akman Akdeniz University Engineering Faculty Biomedical Engineering Department Antalya Turkey

ISBN: (纸本)9789898704214

The aim of this work is to classify Hemangioma, Rosacea and Acne Vulgaris diseases from digital colored photographs automatically. To determine the most appropriate deep neural network for this multi-class classification, network architectures have been examined. To perform a meaningful comparison of deep networks, they should be (i) implemented with the same parameters, (ii) applied with the same activation, loss and optimization functions, (iii) trained and tested with the same datasets, (iv) run on computers having the same hardware configurations. Therefore, in this work, five deep networks, which are applied widely in image classification, have been used to compare their performances by considering these factors. Those networks are VGG16, VGG19, GoogleNet, InceptionV3 and ResNet101. Comparative evaluations of the results obtained from these networks have been performed in terms of accuracy, precision and specificity. F1 score and Matthew's correlation coefficient values have also been computed. Experimental results indicated that ResNet101 architecture can classify images used in this study with higher accuracy (77.72%) than the others. © Proceedings of the 14th IADIS International conference Computer Graphics, visualization, Computer Vision and Image Processing 2020, CGVCVIP 2020 and Proceedings of the 5th IADIS International conference Big Data Analytics, Data Mining and Computational Intelligence 2020, BigDaCI 2020 and Proceedings of the 9th IADIS International conference Theory and Practice in Modern computing 2020, TPMC 2020 - Part of the 14th Multi conference on Computer Science and Information Systems, MCCSIS 2020. All rights reserved.

关键词： Deep neural networks

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Real Time Vein visualization using Near-Infrared Imaging

Real Time Vein Visualization using Near-Infrared Imaging

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2020 International conference on Computational Intelligence, ICCI 2020

作者： May, Hia Yee Ernawan, Ferda Universiti Malaysia Pahang Gambang Faculty of Computing Kuantan Malaysia

ISBN: (纸本)9781728154473

Vein visualization is one of the most researched biomedical technique. Although the concept behind the technique is not complicated, the vein pattern acquisition method and the design and implementation of image processing algorithms become challenging. Nowadays, the major challenge faced by the medical practitioners is the difficulty in accessing subcutaneous veins for intra-venous injections due to various factors like low visibility of vein by naked eyes and patients with too narrow veins. Failure during venipuncture may lead to several problems like bruises, bleeding and rashes. Therefore, the real time vein visualization system is developed accordance with the objective of visualizing subcutaneous veins which is to assist medical practitioners by providing them visual guidance during venipuncture process. This system is developed based on near-infrared imaging and is connected to the monitor screen. The development stage includes edge detection, vein segmentation and vein visualization. Evolutionary prototyping method is used to develop the system and to ensure the quality of the final system through a few prototype refinement cycles. OpenCV library is also used for its real-time functionalities. The functionality of the system is evaluated through a series of planned system tests. The experimental results show that the proposed system is able to show the veins pattern. © 2020 IEEE.

关键词： visualization

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