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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141-150 订阅

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Development of an AI-Based Website for Automated Pre-Operative Planning in Aortic Valve Replacement Using Deep Learning

Development of an AI-Based Website for Automated Pre-Operati...

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International conference on Innovation and Intelligence for Informatics, computing, and Technologies (3ICT)

作者： John Lorance William Nadine Farid Elshafey Nadine Walid Abdallah Nouran Hady Shaaban Sama Ahmed Okasha Mustafa A. Elattar School of ITCS Nile University Cairo Egypt School of ITCS Nile University Alexandria Egypt School of ITCS Nile University Giza Egypt Center for Informatics Science School of ITCS Nile University Giza Egypt

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

This paper introduces a web-based software pipeline to help perform minimally invasive aortic valve replacement (AVR) procedures. The complete pipeline contains three main steps: aortic extraction, automatic landmark detection, and calculation of the specific measurements required for valve replacement. For Computed Tomography Angiography (CTA) scans, separate deep neural network models based on the U-Net architecture in the Medical Open Network for AI (MONAI) framework segment out key anatomical landmarks, including the Sinotubular Junction (STJ), Left Coronary Artery (LCA), Right Coronary Artery (RCA), and Annulus Plane. This improves the accuracy of aorta segmentation and landmark detection. A web interface facilitates easy data upload, visualization of segmentation results, and access to key measurements for preoperative planning. Artificial Intelligence (AI) and machine learning in medical imaging lead to more accurate preoperative planning of Transcatheter Aortic Valve Replacement (TAVR) procedures, reducing the likelihood of complications from improperly fitted valves. There is still more work to be done before the model can be used in cardiovascular disease diagnosis, including refining models, enriching datasets, and integrating several features that are beneficial for cardiologists.

关键词： Image segmentation Accuracy Computational modeling Valves Data models Planning Security Artificial intelligence Medical diagnostic imaging biomedical imaging

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Advanced Attention Capsule Network with Curved Wavelet Attention for Illuminating Low-Light Capsule Endoscopy

Advanced Attention Capsule Network with Curved Wavelet Atten...

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Applied Intelligence and computing (AIC), IEEE World conference on

作者： G. Maheswari S. Gopalakrishnan Department of Information Technology Hindustan Institute of Technology and Science Kelambakkam Tamil Nadu India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

Capsule endoscopy has transformed medical imaging with its non-invasive approach to visualize the gastrointestinal tract. However, challenges persist in low-light conditions, impacting diagnostic accuracy. This paper introduces the Advanced Attention Capsule Network (AACNet), fortified with curved wavelet attention blocks, leveraging dynamic curved parameter estimation, feature rescaling, and strategic pooling. By incorporating Discrete Wavelet Transform (DWT), Inverse Wavelet Transform (IWT), and sigmoid activation, AACNet addresses low-light deficits in capsule endoscopy. This research explores the model's efficiency in handling varying object sizes through a spatial pyramid pooling layer. Channel attention modules and a dynamic channel attention module refine feature representation, enhancing the model's ability to discern intricate details within curved structures. The multi-level capsule network captures hierarchical relationships among features, contributing to a comprehensive solution for enhanced medical imaging. AACNet demonstrates remarkable results in achieving 85% accuracy across diverse anatomical classes like foreign body, lymphangiectasia, ampulla, and fresh blood. Precision values (85%) highlight AACNet's precision in minimizing false positives. Variable recall values showcase the model's adaptability, capturing true positives in distinct scenarios. F1 scores emphasize AACNet's effective balance between precision and recall, which is crucial for robust diagnostics. Specificity values underscore AACNet's accuracy in identifying negative instances.

关键词： Adaptation models visualization Accuracy Parameter estimation Endoscopes Gastrointestinal tract Robustness Discrete wavelet transforms Blood biomedical imaging

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Multi-Task Joint 3D Swin Transformer Learning for Segmentation and Classification of Hyperspectral Medicine Images

Multi-Task Joint 3D Swin Transformer Learning for Segmentati...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dong Zhang Meijun Sun College of Intelligence and Computing Tianjin University Tianjin China Institute of Traditional Chinese Medicine Tianjin University of Traditional Chinese Medicine Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Hyperspectral images had made many applications in the medical field with their rich spectral information. However, there were currently problems with feature extraction based on hyperspectral images, especially in extracting contextual feature information from spectral bands, and a single convolutional kernel may restrict the receptive field and inadequately capture the sequential properties of the data. Meanwhile, due to the large data volume of hyperspectral images, current medical hyperspectral images focus more on individual segmentation or classification. This paper proposed a 3D swin transformer with multi-task joint learning framework, to simultaneously learn multiple tasks for hyperspectral tongue images. Based on the 3D swin transformer model, the framework regards cross-band context feature learning of hyperspectral images as a sequence-to-sequence prediction process. The 3D transformer encoder used as the basic framework for shared feature extraction, and set up corresponding decoders for prediction according to different visual tasks such as segmentation and classification. We conducted simultaneous segmentation and classification tasks of the tongue coating region on the hyperspectral image of tongue images. The results showed that the proposed model had good results in single tasks of segmentation and classification, and performed better than other multi-task convolutional neural network models.

关键词： Training Image segmentation visualization Tongue Three-dimensional displays Transformers Feature extraction Multitasking Hyperspectral imaging biomedical imaging

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Enhancing Oral Cancer Screening with Deep Learning Algorithms

Enhancing Oral Cancer Screening with Deep Learning Algorithm...

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computing, Sciences and Communications (ICCSC), International conference on

作者： Attarde Viren Bhaskar A Soujanya P. Janaki Ramal S. SanthanaLakshmi K Nithyakalyani Bharathi Ramesh Kumar Department of Critical Care Medicine Dr. D.Y. Patil Medical College Hospital & Research Centre Pimpri Pune India Department of Computer Science and Engineering CVR College of Engineering Ibrahimpatnam Telangana India Department of Computer Science Engineering Saveetha Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Mohamed Sathak A.J.college of Engineering Siruseri Chennai India Department of Biomedical Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai Tamil Nadu India Department of Mathematics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

The majority of the conventional approaches used in current oral cancer screening methods, such as biopsy and visual inspection, result in late-stage detection, high false-positive/negative rates, and higher healthcare expenses. These techniques need specialized staff and are labor-intensive and subjective. To improve diagnostic speed and accuracy, the research offers a unique approach that makes use of deep learning methods, particularly Transformer models and Hybrid Neural Networks (HNNs). To enhance input quality, the system incorporates sophisticated image preparation methods such as data augmentation and normalization. While HNNs integrate Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for robust feature extraction and temporal analysis, Transformers use attention techniques to extract contextual information. Its potential for early and accurate oral cancer detection is highlighted by the results, which show a significant improvement in detection accuracy (94% vs. 80-85% in existing systems), sensitivity (90% vs. 70-78%), and specificity (92% vs. 75-82%), along with a reduction in processing time to just 2 minutes.

关键词： Deep learning visualization Accuracy Sensitivity Computational modeling Medical services Transforms Transformers Feature extraction Cancer

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Pneumonia Diagnosis in Chest X-rays: A Deep Learning Framework Using Mask R-CNN and ResNet

Pneumonia Diagnosis in Chest X-rays: A Deep Learning Framewo...

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Advances in computing, Communication and Networking (ICAC2N), International conference on

作者： Jay Vithlani Kanchan Dabre Pratham Gala Satishkumar L. Varma Dept. of Computer Science and Engineering(Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India Dept. of Computer Science and Engineering(Data Science) Mumbai India Pillai College of Engineering Mumbai India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Pneumonia remains a significant factor in global mortality rates, highlighting the need for prompt and accurate diagnostic methods to improve patient outcomes. While radiographic examination is a standard diagnostic tool, accurate chest X-ray interpretation might be difficult because of the visual similarities between pneumonia and other respiratory issues. This research explores deep learning techniques for detecting and localizing pneumonia in chest X-ray images. This work has investigated Mask R-CNN’s performance with various backbone architectures. This approach includes comprehensive data preparation, including text removal, image resizing, and employing data augmentation techniques. The study examines different backbone architectures, loss functions, and optimization strategies to find the optimal configuration. The bounding boxes generated by the models identify potential pneumonia infections, and their performance is evaluated using various metrics at IoU thresholds between 0.5 and 0.95. The combined results of the Mask R-CNN model as base model and backbone model as ResNet-101 demonstrates superior performance, with an average IoU of 0.75, precision (88.21%), recall (86.03%), and an F1 score (87.11%). This proposed method not only performs competitively with current models but also provides precise localization through bounding boxes and segmentation masks, potentially enhancing the diagnostic process for radiologists. This research represents a significant advancement in utilizing deep learning for detecting and localizing pneumonia in CXR-images.

关键词： Deep learning Analytical models visualization Pneumonia Accuracy Reliability X-ray imaging Standards Optimization biomedical imaging

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A Smart Eyewear using IoT and CNNs for Visual Assistance

A Smart Eyewear using IoT and CNNs for Visual Assistance

引用

Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International conference on

作者： Chitra Sabapathy Ranganathan V. Kannagi R C Karpagalakshmi N V Shibu Ishwarya M. V S. Murugan Mphasis Corporation Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Alliance College of Engineering and Design Alliance University Bengaluru Karnataka India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Artificial Intelligence and Data science Department Agni College of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

The development of Convolutional Neural Networks (CNNs) and the growth of the Internet of Things (IoT) have made smart eyewear a potentially revolutionary platform for visual assistance. This research introduces a new method for real-time object detection and navigation assistance using IoT connection and CNNs algorithms. The proposed solution improves users' ability to see and understand their environments by combining a small camera with smart glasses. The smart glasses assist the user by identifying items in their range of vision using CNN-based object recognition algorithms and giving contextual information. Using IoT connection, the system allows easy integration with navigation services, providing users with real-time assistance and direction in new areas. It presents the planned smart eyewear system, including its design, implementation, and assessment. It emphasizes the system's uses in helping people with vision impairments or navigating complicated situations more independently. The experimental findings show that the system is efficient and effective, with reliable visual and navigational help, proving it can be used in the real world.

关键词： visualization Navigation Visual impairment Object detection Real-time systems Internet of Things Reliability Object recognition Convolutional neural networks Smart glasses

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Edge-computing Enabled Real-Time Respiratory Monitoring and Breathing Pattern Detection

Edge-Computing Enabled Real-Time Respiratory Monitoring and ...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Ucchwas Talukder Utsha Bashir I. Morshed Department of Computer Science Texas Tech University Lubbock TX USA

ISBN: (数字)9798331530143

ISBN: (纸本)9798331530150

Detecting respiratory disorders can be improved with real-time diagnostic solutions, where monitoring respiration rates and patterns can act as early indicators for various cardiorespiratory diseases. We present a cost-effective edge-computing method utilizing wearable technology for respiratory disorder detection. Our system employs a wearable device with an IMU sensor for real-time signal transmission to a custom smart-phone app which enables thorough visualization of respiratory signals, continuous monitoring of respiration rates, and rapid alarms for breathing anomalies, alongside ECG functionalities. A novel approach is introduced for respiratory pattern detection that includes a pre-trained AI model for apnea detection and classification of normal, bradypnea, and tachypnea patterns from non-apnea signals. During model building using the Apnea-ECG dataset, the proposed hyper-feature algorithm for apnea detection demonstrates excellent performance, achieving accuracies of 92.33%, 94.89%, and 97.66% for Chest, Abdominal, and Nasal respiration signals, respectively. With an average inference time of 5 ms for respiratory event detection, the classifier achieves outstanding accuracy rates of 91.24%, 92.42%, and 93.26% for these signals on the validation dataset after being implemented and tested at the edge device. Real-time data analysis from 10 subjects further underscores the system's potential for continuous respiratory and cardiac monitoring in real-world scenarios.

关键词： Accuracy Image edge detection Nose Medical services Feature extraction Real-time systems Mobile applications biomedical monitoring Wearable devices Monitoring

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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IEEE EMBS International conference on Information Technology Applications in Biomedicine (ITAB)

作者： Shun-Cheng Chang Hsin-Pei Yu Yi-Hsien Hsieh Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan Institute of Applied Mathematical Sciences NTU Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag.

关键词： visualization Accuracy Three-dimensional displays Liver Transformers Planning Convolutional neural networks Context modeling biomedical imaging Tumors

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Remote Monitoring and Analytics For Cloud-Based Drip Saline Fluid Management System

Remote Monitoring and Analytics For Cloud-Based Drip Saline ...

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Smart Electronics and Communication (ICOSEC), International conference on

作者： J. Gnanasoundharam G. Sudha J. Alphas Jeba Singh M. Birunda E. N. Ganesh Department of Electronics and Instrumentation Engineering St. Joseph’s College of Engineering Chennai Tamil Nadu India Department of Biomedical Engineering Muthayammal Engineering College(Autonomous) Namakkal Tamil Nadu India Department of Electronics and Communication Engineering Einstein College of Engineering Tirunelveli Tamil Nadu India Department of Electronics and Communication Engineering St. Peters Institute of Higher Education and Research (SPIHER) Chennai Tamil Nadu India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Drip saline fluid administration in hospitals can be better monitored and managed with the help of cloud-based technology. Intravenous treatment and postoperative recovery are two instances when drip saline fluid therapy is essential. The proposed system uses the Internet of Things (IoT) to remotely monitor vital metrics, including fluid flow rate, infused volume, and patient vital signs while delivering saline fluids. Using Raspberry Pi, the system gathers sensor data, processes it, sends it to the cloud, integrates it with the cloud, and triggers abnormal notification alerts. Accurate and timely fluid delivery can significantly improve patient health and reduce the risk of complications when monitored manually. When the system detects any abnormal condition, such as a drop in the predicted flow rate or an unexpected and concerning shift in patient vital signs, it sends a message to the relevant medical personnel. Healthcare providers can better manage their patients’ fluid treatment with accurate and up-to-date information through cloud-based analytics. Insights and suggestions for action are gathered from the data by smart systems. Doctors can detect patterns, predict difficulties, and optimize their patients’ fluid management plans by analyzing data. The system’s remote access features allow doctors to check patients and track their fluid treatment from anywhere. Improved patient outcomes and more efficiency in healthcare settings are the ultimate objectives of this system.

关键词： Cloud computing visualization Fluids Accuracy Medical services Real-time systems Internet of Things biomedical monitoring Monitoring Testing

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Motion tracked 3D visualization system for Segmented ROI in medical imaging 2021

Motion tracked 3D Visualization system for Segmented ROI in ...

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7th International conference on Arab Women in computing, ArabWIC 2021

作者： Yaghi, Maha Zia, Huma Hajjdiab, Hassan El-Baz, Ayman Ghazal, Mohammed Electrical Computer and Biomedical Engineering Department Abu Dhabi University United Arab Emirates Computer Science Department Concordia University Bioengineering Department University of Louisville

ISBN: (纸本)9781450384186

Computer-aided diagnosis systems are increasingly used in the detection and segmentation of abnormalities in medical imaging. However, in many borderline cases, radiologists and physicians need to analyze the images to confirm the diagnosis. This paper proposes a novel analysis system for visualizing the output of 3D segmented abnormalities and the associated computer diagnoses. We use motion trackers to localize the user's viewing device and estimate its 3D orientation. We then use this tracking data to slice the segmented and labeled volume showing the abnormalities in real-time. The user can switch between the labeled and raw views to analyze the abnormalities' boundaries. To test our visualization, we use our brain tumor segmentation pipeline using MRI scans to produce a labeled image and slice and visualize the tumor from all angles. Our system has a low latency of 0.01 seconds and can identify the viewing device's 3D location and orientation within a maximum of 0.2 cm and 2 degrees of error, respectively. The accuracy of our system depends on the segmentation pipeline. Our proposed system is also useful for medical training and as a visual feedback tool for computer-aided system's researchers and developers. © 2021 Association for computing Machinery.

关键词： Magnetic resonance imaging

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