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

共 898 条记录，以下是91-100 订阅

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Structural Homology of Epitope Binding Mimicry in the Onset of Type 1 Diabetes Mellitus

Structural Homology of Epitope Binding Mimicry in the Onset ...

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2023 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Gardner, Ryan Wilkins, Joshua Mistry, Sejal Gouripeddi, Ramkiran Facelli, Julio C. Weber State University OgdenUT United States North Carolina a&t GreensboroNC United States University of Utah Department of Biomedical Informatics Salt Lake CityUT United States University of Utah Department of Biomedical Informatics Utah Clinical and Translational Science Institute Salt Lake CityUT United States

ISBN: (纸本)9798350337488

Molecular mimicry, where foreign and self-peptides contain similar epitopes, can induce autoimmune responses. Identifying potential molecular mimics and studying their properties is key to understanding the onset of autoimmune diseases such as type 1 diabetes mellitus (T1DM). Previous work identified pairs of infectious epitopes (EINF) and T1DM epitopes (ET1D) that demonstrated sequence homology;however, structural homology was not considered. Correlating sequence homology with structural properties is important for streamlining translational investigation of potential molecular mimics. Therefore, the purpose of this work is to compare sequence homology with structural homology by calculating the structures and electrostatic potentials of 35 pairs of epitopes identified in previous work from our laboratory. For each epitope pair the root mean square deviation (RMSD) was calculated between their predicted structures and their electrostatic potentials were compared. Structures were predicted using the AlphaFold and I-TASSER software programs. We considered a structural match of EINF and ET1D pairs successful if the RMSD was INF/ET1D structurally unmatched pairs. Despite structural differences, these four EINF/ET1D pairs show similar electrostatic distributions, indicating that they may still bind to the same protein targets, major histocompatibility complex molecules, for T1DM. These findings suggest that searching for epitope pairs using sequence homology, a much less computationally demanding approach, leads to strong candidates for further study. © 2023 IEEE.

关键词： Islet Autoimmunity Molecular Mimicry Protein Structure Prediction Type 1 Diabetes Mellitus

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Parsed Region Feature Extraction Method for Hepatocellular Carcinoma Detection Using Medical Images

Parsed Region Feature Extraction Method for Hepatocellular C...

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Smart Technologies and Systems for Next Generation computing (ICSTSN), International conference on

作者： T Thangam A. Kaleel Rahuman J. G. Sabarish S. Sudhakar P. Jeyalakshmi Department of ECE PSNA College of Engineering and Technology Dindigul India Department of ECE NPR College Of Engineering and Technology Dindigul India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

Hepatocellular carcinoma detection using medical images relies on precise feature selection from the set of extracted ones that augments high detection accuracy. This article introduces a Parsed Region Feature Extraction Method (PRFEM) to improve the carcinoma detection accuracy from medial input images. The proposed method utilizes a two-layer convoluted neural network with a linear pooling function to select optimal features for detection. The first layer is responsible for selecting visual pattern-based features that improve the region detection. In the second layer, the identified features are verified for their missing distribution sequences that are useful in retaining precision. The linear pooling identifies the distribution break points to train the CNN with the need for high feature selection. This improves the detection precision with minimum errors. The proposed PRFEM improves detection precision, feature selection, and accuracy by 11.7%, 9.4%, and 10.5% respectively for the varying patterns.

关键词： visualization Accuracy Neural networks Feature extraction Next generation networking biomedical imaging

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Algorithm Research of ELMo Word Embedding and Deep Learning Multimodal Transformer in Image Description

Algorithm Research of ELMo Word Embedding and Deep Learning ...

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IEEE International conference on Power, Intelligent computing and Systems (ICPICS)

作者： Xiaohan Cheng Taiyuan Mei Yun Zi Qi Wang Zijun Gao Haowei Yang Northeastern University Boston USA Georgia Institute of Technology Atlanta USA University of Houston Houston USA

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Zero sample learning is an effective method for data deficiency. The existing embedded zero sample learning methods only use the known classes to construct the embedded space, so there is an overfitting of the known classes in the testing process. This project uses category semantic similarity measures to classify multiple tags. This enables it to incorporate unknown classes that have the same meaning as currently known classes into the vector space when it is built. At the same time, most of the existing zero sample learning algorithms directly use the depth features of medical images as input, and the feature extraction process does not consider semantic information. This project intends to take ELMo-MCT as the main task and obtain multiple visual features related to the original image through self-attention mechanism. In this paper, a large number of experiments are carried out on three zero-shot learning reference datasets, and the best harmonic average accuracy is obtained compared with the most advanced algorithms.

关键词： visualization Image recognition Accuracy Semantics Zero shot learning Feature extraction Transformers Vectors biomedical imaging Testing

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Wearable Pain Detector Using Heart Rate and Skin Conductance

Wearable Pain Detector Using Heart Rate and Skin Conductance

引用

computing (ICOCO), IEEE International conference on

作者： Fadli Maulana Bagas Daffa Torrie P.H Granatha Anugrah Wahmisari Priharti Hesty Susanti School of Electrical Engineering Telkom University Bandung Indonesia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

Pain not only impacts the physical condition but is also influenced by physiological factors that play a role in amplifying or influencing pain. A common approach used to manage pain is the use of opioids. The use of opioids in pain management has risks, especially overdose due to inappropriate use. One factor that can contribute to overdose is the lack of objective pain assessment. One method to assess pain is the visual analog scale. Due to its subjective nature, this can lead to inconsistent and inaccurate assessments. To overcome this impact, a wearable pain detector was developed that is designed to detect pain by combining two physiological parameters, namely heart rate activity and skin conductance, which produces a pain scale with a range of 0-10, and provides classification with categories ranging from “no pain” to “most severe pain”. The classification process is performed using the simple linear regression method. Overall, the wearable pain detector had an accuracy of 75% in calculating the pain scale of the subject. The product is designed in the form of a wearable device that resembles a watch, making it comfortable for patients to use.

关键词： Heart rate visualization Accuracy Pain Detectors Opioids Skin biomedical monitoring Wearable devices Monitoring

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Diffusion Model with Self-supervised Training for Subject-aware Photoplethysmography Recovery

Diffusion Model with Self-supervised Training for Subject-aw...

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IEEE biomedical Circuits and Systems (BIOCAS)

作者： Daomiao Wang Qihan Hu Xujian Feng Cuiwei Yang Center for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

Photoplethysmography (PPG) has emerged as a promising technology for wearable personal healthcare due to its non-invasive and cost-effective nature. However, owing to the vulnerability to various noise, the useful PPG signals are few and the usability of PPG-based systems are limited. In this paper, we address the challenge of recovering extremely corrupted PPG waveforms with severe distortions, which lead to missing data. Specifically, we propose a novel diffusion-based framework that incorporates remaining values and subject-aware embedding, along with a self-supervised generative training strategy. Extensive experiments were performed on two datasets under three missing rates and two missing patterns. Our proposed model achieves significant improvements of nearly 100% over existing imputation models on quantitative metrics, and together with the visualization, demonstrating its effectiveness for data distribution generation and corrupted PPG restoration. Our proposed method also provides a new perspective for PPG denoising.

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Survey of Large Language Models for Answering Questions Across Various Fields

Survey of Large Language Models for Answering Questions Acro...

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International conference on Advanced computing and Communication Systems (ICACCS)

作者： Pasi Shailendra Rudra Chandra Ghosh Rajdeep Kumar Nitin Sharma Central Research Laboratory Bharat Electronics Limited Ghaziabad India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

In recent years, the burgeoning popularity of language models stems from their demonstrably superior performance compared to traditional machine learning techniques and conventional deep learning models across various Natural Language Processing (NLP) tasks. Despite their remarkable achievements, large language models (LLMs) are not immune to constraints, such as sporadic generation of inaccurate responses, particularly noticeable in intricate queries. Presently, LLMs find extensive application across diverse domains, with one prominent example being question-answering tasks, wherein the model is tasked with generating responses based on provided input questions and context. The intricacy escalates when the model operates in a context-agnostic manner. This study presents a thorough examination of question-answering tasks across different domains, including Open domain, Medical domain, Visual, and Multi-lingual question-answering. We delve into the latest advancements in LLMs across these domains, offering nuanced insights into their efficacy and limitations.

关键词： Surveys visualization Large language models Computational modeling Oral communication Question answering (information retrieval) Tuning Context modeling biomedical imaging Immune system

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Recent Advancements on Intelligent Multi-focus Image Fusion

Recent Advancements on Intelligent Multi-focus Image Fusion

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

作者： Prabhishek Singh Aayushi Rana Aditya Joshi Mohd Shuaib Manoj Diwakar Amit Kumar Mishra School of Computer Science Engineering & Technology Bennett University Greater Naida India Department of Computer Application Graphic Era Deemed to be University Dehradun India Department of CSE Graphic Era Deemed to be University Dehradun India Dehradun India Department of CSE Graphic Era Hill University Dehradun India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Recently deep learning approaches are more popular for multi-focus image fusion. In this review article, non-conventional multi focus image fusion techniques recently published were analyzed in detail and current advances as well as impending trends were discussed. The push for ever increasing image quality in multi focus imaging has raised the desire for non-conventional techniques as possible substitutes to conventional techniques. In this review, we systematically examine state of the art research in deep learning models, hybrid methodologies combining multiple transform domains, and adaptive thresholding strategies. We conducted a comparison evaluation of visual data against different strategies utilizing the complementary fusion capabilities of each. In addition, quantitative metrics of these values were used to objectively measure the performance of each strategy. In the paper, important ongoing developments, such as integration of deep learning techniques with transformer methods and adaptive fusion filters to enhance image details while avoiding artefacts are stressed. The implications of this review for academics include a comparison of the progress in the field to date and the potential of non classical techniques to realize higher accuracy and reliability in image fusion. The results suggest that further investigation of more sophisticated hybrid and adaptive techniques will yield improved diagnostic quality in a clinical setting.

关键词： Deep learning Image quality visualization Adaptation models Filters Accuracy Reviews Transforms Market research Image fusion

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Simplifying Graph-Parallel Computation in Apache Spark with GraphX

Simplifying Graph-Parallel Computation in Apache Spark with ...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Lasya Vyakaranam Ramakrishnan Raman Harshal Shah Nilamadhab Mishra R. Meenakshi S. Murugan Symbiosis Law School (SLS) Pune Symbiosis International (Deemed University) Pune Maharashtra India Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Vadodara Gujarat India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Computer Science and Engineering Chennai Institute 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)9798350367904

ISBN: (纸本)9798350367911

Streamlining Apache graph-parallel computing the goal of integrating Spark with GraphX is to make processing massive amounts of graph data easier and faster. The goal is to streamline the process of creating and running graph algorithms by using GraphX, a Spark-based distributed graph processing platform. The eventual objective is to create a scalable platform that is easy to use for graph-structured data analysis and manipulation. Improving GraphX’s performance, expanding its features, and integrating it with Spark’s ecosystem are all part of this process. The expected result is a flexible and strong framework that lets data scientists and developers execute complicated graph analytics tasks with ease. This will pave the way for applications in many fields, including recommendation systems, social network analysis, and biological network analysis. Optimizing performance in graph-parallel computer situations, making the development process easier, and making it more scalable are the main goals. The graph structure visualization results in graph data revealing values in the range of 1.2 to 5.6 for a sample of five distinct vertex/edge types. The degree distribution histogram in the same dataset shows that, for five different frequencies, the numbers range from around 1.5 to 4.5. For the same dataset, the PageRank Convergence Plot shows values ranging from 0.4 to 0.9 for five distinct iterations/scores.

关键词： Histograms Technological innovation Social networking (online) Scalability Data visualization Cluster computing Distance measurement Sparks Recommender systems Convergence

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A Hybrid Transformer-Deep Learning Model for Improved Cardiac MRI Left Ventricle Segmentation

A Hybrid Transformer-Deep Learning Model for Improved Cardia...

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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Kh Tohidul Islam Syed Mohammed Shamsul Islam Md Moniruzzaman Abdul Ihdayhid Discipline of Computing and Security School of Science Edith Cowan University Perth Australia Monash Biomedical Imaging Monash University Clayton Australia Curtin Medical School Curtin University Perth Australia Department of Cardiology Fiona Stanley Hospital Perth Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Cardiac left ventricle segmentation is crucial in diagnosing and managing various cardiac diseases. Accurate and efficient segmentation c an leadtop recise e valuations of left ventricular size, shape, and function, ultimately enhancing clinical outcomes. With the advent of deep learning, transformer architectures have emerged as powerful tools for medical image segmentation. This study introduces an innovative hybrid architecture that combines the strengths of the transformer model with established deep-learning models. Our approach demonstrates superior performance in cardiac left ventricle segmentation from MRI images, achieving near-perfect accuracy and significantly outperforming traditional models in both Dice coefficient and Intersection over Union (IoU) metrics. These results confirm the model's ability to capture complex image dependencies while maintaining class balance, marking a notable advancement in cardiac MRI segmentation.

关键词： Measurement Deep learning Image segmentation visualization Accuracy Magnetic resonance imaging Computer architecture Streaming media Transformers Medical diagnostic imaging

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Optimized Reversible Data Hiding with CNN Prediction and Enhanced Payload Capacity

Optimized Reversible Data Hiding with CNN Prediction and Enh...

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International conference on Circuits, Communication, Control and computing

作者： Geetha R D Jenila Rani Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

The security of digital data is guaranteed while processing and storage in encrypted cloud storage thanks to Reversible Data Hiding (RDH). During this process, users stop cloud providers (CP) from accessing image data. This paper presents a high-capacity RDH technique for safe media transmission. Based on pixel parity, the image is first divided into two sub-images. First sub-image is made up of odd pixels, and second is made up of even pixels. The Most Significant Bit (MSB) of the pixels for embedding is predicted using a Convolutional Neural Network (CNN) predictor. The secret information is encoded in the embedding using a plus/minus one technique. The cover image can be recovered with the help of an encryption key that is shared by the sender and recipient. CNN predictors use the plus-minus one technique to optimise the embedding strategy. They are renowned for their improved receptive mechanisms and increased utilisation of neighbouring pixels. Promising results are obtained by combining the CNN predictor with the plus-minus one embedding strategy, which outperforms the existing methods in terms of both visual quality and embedding capacity. For example, the average Peak Signal-to-Noise Ratio (PSNR) value for 15,000 bits on the Kodak dataset reaches an astounding 67 dB.

关键词： Image segmentation Cloud computing visualization PSNR Image coding Media Complexity theory Convolutional neural networks Optimization Payloads

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