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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yulin Yang Yinhao Li Qingqing Chen Xian-Hua Han Jing Liu Lanfen Lin Hongjie Hu Yen-Wei Chen Gradate School of Information Science and Engineering Ritsumeikan University Shiga Japan College of Information Science and Engineering Ritsumeikan University Shiga Japan Department of Radiology Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China Artificial Intelligence Research Center Yamaguchi University Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

Compared to non-contrast computed tomography (NC-CT) scans, contrast-enhanced (CE) CT scans provide more abundant information about focal liver lesions (FLLs), which play a crucial role in the FLLs diagnosis. However, CE-CT scans require patient to inject contrast agent into the body, which increase the physical and economic burden of the patient. In this paper, we propose a spatial attention-guided generative adversarial network (SAG-GAN), which can directly obtain corresponding CE-CT images from the patient’s NC-CT images. In the SAG-GAN, we devise a spatial attention-guided generator, which utilize a lightweight spatial attention module to highlight synthesis task-related areas in NC-CT image and neglect unrelated areas. To assess the performance of our approach, we test it on two tasks: synthesizing CE-CT images in arterial phase and portal venous phase. Both qualitative and quantitative results demonstrate that SAG-GAN is superior to existing GANs-based image synthesis methods.

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Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

arXiv

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arXiv 2023年

作者： Huang, Zhuo Zhu, Miaoxi Xia, Xiaobo Shen, Li Yu, Jun Gong, Chen Han, Bo Du, Bo Liu, Tongliang Syndey Ai Centre The University of Sydney Australia National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Jd Explore Academy China Department of Automation University of Science and Technology of China China Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Hong Kong Baptist University Hong Kong

Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common solutions such as distributionally robust optimization (DRO) focus on the worst-case empirical risk to ensure low training error on the uncommon noisy distributions. However, due to the over-parameterized model being optimized on scarce worst-case data, DRO fails to produce a smooth loss landscape, thus struggling on generalizing well to the test set. Therefore, instead of focusing on the worst-case risk minimization, we propose SharpDRO by penalizing the sharpness of the worst-case distribution, which measures the loss changes around the neighbor of learning parameters. Through worst-case sharpness minimization, the proposed method successfully produces a flat loss curve on the corrupted distributions, thus achieving robust generalization. Moreover, by considering whether the distribution annotation is available, we apply SharpDRO to two problem settings and design a worst-case selection process for robust generalization. Theoretically, we show that SharpDRO has a great convergence guarantee. Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization

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QUANTIFYING WHITE MATTER HYPERINTENSITY AND BRAIN VOLUMES IN HETEROGENEOUS CLINICAL AND LOW-FIELD PORTABLE MRI

arXiv

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arXiv 2023年

作者： Laso, Pablo Cerri, Stefano Sorby-Adams, Annabel Guo, Jennifer Mateen, Farrah Goebl, Philipp Wu, Jiaming Liu, Peirong Li, Hongwei Young, Sean I. Billot, Benjamin Puonti, Oula Sze, Gordon Payabavash, Sam DeHavenon, Adam Sheth, Kevin N. Rosen, Matthew S. Kirsch, John Strisciuglio, Nicola Wolterink, Jelmer M. Eshaghi, Arman Barkhof, Frederik Kimberly, W. Taylor Iglesias, Juan Eugenio Massachusetts General Hospital Harvard Medical School United States Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Pioneer Centre for AI University of Copenhagen Denmark Centre for Medical Image Computing University College London United Kingdom Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Danish Research Centre for Magnetic Resonance Denmark Yale School of Medicine United States

Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis. Automated segmentation and quantification is desirable but existing methods require high-resolution MRI with good signal-to-noise ratio (SNR). This precludes application to clinical and low-field portable MRI (pMRI) scans, thus hampering large-scale tracking of atrophy and WMH progression, especially in underserved areas where pMRI has huge potential. Here we present a method that segments white matter hyperintensity and 36 brain regions from scans of any resolution and contrast (including pMRI) without retraining. We show results on eight public datasets and on a private dataset with paired high- and low-field scans (3T and 64mT), where we attain strong correlation between the WMH (ρ=.85) and hippocampal volumes (ρ=.89) estimated at both fields. Our method is publicly available as part of FreeSurfer, at: http://***/fswiki/WMH-SynthSeg. © 2023, CC BY.

关键词： Neuroimaging

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Expression of Concern for: Investigating the Benefits of Machine Learning Algorithms for Text Classification

Expression of Concern for: Investigating the Benefits of Mac...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Gowrishankar J Vinithra Banu. T Parikshit N Vandana Singh Hitesh Kalra Girija Shankar Sahoo Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

This paper affords a research into using gadget studying algorithms for textual content category duties, with a focal point on understanding the importance of these algorithms and their ability to enhance the accuracy of textual content classification. it's miles located that, while conventional techniques, which include rule based models and hand-engineered features, can attain first-class outcomes, there are compelling motives to explore the capability of device getting to know algorithms to further improve the accuracy and performance of textual content class. The paper discusses the 3 maximum famous gadget gaining knowledge of algorithms for this project: the NaÏve Bayes classifier, help Vector Machines, and synthetic Neural Networks, and presents a top level view of the latest research findings that demonstrate the prevalence of these algorithms over traditional methods. The paper additionally presents an outline of the distinctive approaches for introducing era into text type, and discusses modern-day strategies for implementing and deploying those algorithms. Finally, the paper outlines ability areas for destiny research associated with information the results of system studying algorithms for this mission, and suggests viable instructions for exploration.

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Long Term Electricity Load Forecasting for Garhwal Region of Uttarakhand Using artificial Neural Network

Long Term Electricity Load Forecasting for Garhwal Region of...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Rakesh Kumar Rakesh Ranjan Digvijay Singh Nagendar Yamsani Department of Engineering Himgiri Zee University Dehradun India Vice Chancellor Sushant University Gurgaon India Centre for Energy and Eco-Sustainabilty Resarch Uttaranchal University Dehradun India School of Computer Science and Artificial Intelligence SR University Warangal India

The one of the hill state Uttarakhand of India is full of flaura and fauna. The Char Dham Yatra of the state is very famous. Rishikesh city famous for Yog Sadhna and Haridwar is for pilgrims. Dehradun City is the capital of Uttarakhand where is also known as Smart City. The Ministry of Housing and Urban Affairs (MoHUA) has listed Dehradun Smart City among third round cities in India. The initiative Electric Bus introduces Dehradun Smart City as carbon free city. There are 30 Electric Buses planned in Phase-1. Each Electric Bus is equipped with GPS, CCTV Surveillance and Emergency Panic Button. The estimated arrival time (ETA) is linked to Smart Bus Shelters where a digital screen is showing its arrival and waiting time and current position in the city. There are 49 Smart Traffic junctions in the Dehradun Smart City which are connected with Integrated Command and Control centre (ICCC). The arrangement for charging these electric buses have been arranged near the ISBT area. Each Electric Bus takes 2–3 hours for full charging. Vision 2030 is the mission of Uttarakhand Government which includes Electric Vehicle Policy of Uttarakhand Government and Shoonaya Mission of Government of India through which by 2040 sixty percent vehicle will be electric. For smooth functioning of Electric Vehicles charging infrastructure is essential and demand of electricity will be high. There would be high demand of electricity due to invasion of Electric Vehicles. The Generation and Distribution Company needs to predict the electricity demand on Long Terms in Uttarakhand. In this paper the study focuses on five years electricity Load Forecasting. The MATLAB tool is used to predict the demand using Scaled Conjugate Method which uses Deep Learning algorithm.

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Investigating the Benefits of Machine Learning Algorithms for Text Classification

Investigating the Benefits of Machine Learning Algorithms fo...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

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SVM-based Predictive Modeling of Drowsiness in Hospital Staff for Occupational Safety Solution via IoT Infrastructure

SVM-based Predictive Modeling of Drowsiness in Hospital Staf...

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computer, Communication and Control (IC4), International Conference on

作者： B. Jai Ganesh P. Vijayan V. Vaidehi S. Murugan R. Meenakshi M. Rajmohan Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamil Nadu India Department of Computer Science Vinayaka Mission’s Kirupananda Variyar Arts and Science College Vinayaka Mission’s Research Foundation (DU) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

This research proposes an efficient approach to improve hospital worker safety on the job by detecting and preventing drowsiness. The system incorporates data from hospital-deployed Internet of Things (IoT) infrastructure and using Support Vector Machines (SVM) as its primary predictive modeling tool. data pertaining to ambient factors, work habits, and physiological indications of employees are collected in real-time by the IoT infrastructure. To identify patterns linked to sleepiness, the SVM model is trained on an extensive dataset. The next step is to implement the model in a healthcare setting so that it can detect signs of sleepiness and act accordingly in a timely manner. With the help of the IoT the predictive model can be dynamically adjusted and monitored in real-time, making it useful in a wide range of situations. The recommended approach is to enhance workplace safety by reducing the likelihood of accidents caused by sleepiness among healthcare workers. The SVM-based prediction model can correctly detect and forecast cases of sleepiness, the research subjected it to extensive testing and validation. This system's adoption helps with the creation of data-driven, proactive methods to improve hospital occupational safety.

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Implementing Automated Reasoning in Natural Language Processing

Implementing Automated Reasoning in Natural Language Process...

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International Conference on Futuristic Technologies (INCOFT)

作者： Kalyan Acharjya Rakshith N N. Thangarasu Hemalatha S Varsha D Adarsh Srivastava Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer ScienceKarpagam Academy of Higher Education Coimbatore Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Science & Engineering Vivekananda Global University Jaipur India

herbal language processing (NLP) is an unexpectedly advancing field state-of-the-art synthetic intelligence and system ultra-modern. It's been able to permit computer systems to apprehend human language and to perform numerous tasks, from textual content type to automated summarization. Automatic reasoning is one of the most advanced and powerful packages modern-day NLP that promises to unlock the electricity cutting-edge computer systems to recognize and answer questions posed in herbal language. Automatic reasoning systems can analyze a fixed ultra-modern statistics after which reason logically and efficaciously based at the information supplied. This paper will speak the demanding situations latest enforcing automated reasoning, and the way improvements in NLP are getting used to enhance the accuracy and pace ultra-modern automatic reasoning. It'll additionally explore the packages contemporary automated reasoning in various domain names, along with question answering and choice making. finally, it'll discuss the capability for automatic reasoning to revolutionize the sector cutting-edge natural language processing by means of advancing the in a ramification modern methods.

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COVID19 detection in chest x-ray using vision-transformer with different patch dimensions

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Procedia computer science 2024年 235卷 3438-3446页

作者： Seifedine Kadry Laith Abualigah Rubén González Crespo Elena Verdú Robertas Damasevicius Vijendra Singh Venkatesan Rajinikanth Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de La Rioja Logroño 26006 Spain Department of Applied Informatics Vytautas Magnus University 53361 Kaunas Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India

computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays.

关键词： Covid19 X-ray Vision-transformer Patch-size Classification

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Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation

arXiv

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arXiv 2024年

作者： Ye, Maoyuan Zhang, Jing Liu, Juhua Liu, Chenyu Yin, Baocai Liu, Cong Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China IFLYTEK Research IFLYTEK CO. LTD. China College of Computing & Data Science Nanyang Technological University #32 Block N4 #02a-014 50 Nanyang Avenue 639798 Singapore

The Segment Anything Model (SAM), a profound vision foundation model pretrained on a large-scale dataset, breaks the boundaries of general segmentation and sparks various downstream applications. This paper introduces Hi-SAM, a unified model leveraging SAM for hierarchical text segmentation. Hi-SAM excels in segmentation across four hierarchies, including pixel-level text, word, text-line, and paragraph, while realizing layout analysis as well. Specifically, we first turn SAM into a high-quality pixel-level text segmentation (TS) model through a parameter-efficient fine-tuning approach. We use this TS model to iteratively generate the pixel-level text labels in a semi-automatical manner, unifying labels across the four text hierarchies in the HierText dataset. Subsequently, with these complete labels, we launch the end-to-end trainable Hi-SAM based on the TS architecture with a customized hierarchical mask decoder. During inference, Hi-SAM offers both automatic mask generation (AMG) mode and promptable segmentation (PS) mode. In the AMG mode, Hi-SAM segments pixel-level text foreground masks initially, then samples foreground points for hierarchical text mask generation and achieves layout analysis in passing. As for the PS mode, Hi-SAM provides word, text-line, and paragraph masks with a single point click. Experimental results show the state-of-the-art performance of our TS model: 84.86% fgIOU on Total-Text and 88.96% fgIOU on TextSeg for pixel-level text segmentation. Moreover, compared to the previous specialist for joint hierarchical detection and layout analysis on HierText, Hi-SAM achieves significant improvements: 4.73% PQ and 5.39% F1 on the text-line level, 5.49% PQ and 7.39% F1 on the paragraph level layout analysis, requiring 20× fewer training epochs. The code is available at Hi-SAM. Copyright © 2024, The Authors. All rights reserved.

关键词： Masks

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