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Influence of early through late fusion on pancreas segmentation from imperfectly registered multimodal magnetic resonance imaging

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Journal of Medical Imaging 2025年第2期12卷

作者： Remedios, Lucas W. Liu, Han Remedios, Samuel W. Zuo, Lianrui Saunders, Adam M. Bao, Shunxing Huo, Yuankai Powers, Alvin C. Virostko, John Landman, Bennett A. Vanderbilt University Department of Computer Science NashvilleTN United States Johns Hopkins University Department of Computer Science BaltimoreMD United States National Institutes of Health Department of Radiology and Imaging Sciences BethesdaMD United States Vanderbilt University Department of Electrical and Computer Engineering NashvilleTN United States Vanderbilt University Medical Center Department of Medicine Division of Diabetes Endocrinology and Metabolism NashvilleTN United States VA Tennessee Valley Healthcare System NashvilleTN United States Vanderbilt University Department of Molecular Physiology and Biophysics NashvilleTN United States University of Texas at Austin Dell Medical School Department of Diagnostic Medicine AustinTX United States University of Texas at Austin Livestrong Cancer Institutes Dell Medical School AustinTX United States University of Texas at Austin Department of Oncology Dell Medical School AustinTX United States University of Texas at Austin Oden Institute for Computational Engineering and Sciences AustinTX United States Vanderbilt University Department of Biomedical Engineering NashvilleTN United States

Purpose: Combining different types of medical imaging data, through multimodal fusion, promises better segmentation of anatomical structures, such as the pancreas. Strategic implementation of multimodal fusion could improve our ability to study diseases such as diabetes. However, where to perform fusion in deep learning models is still an open question. It is unclear if there is a single best location to fuse information when analyzing pairs of imperfectly aligned images or if the optimal fusion location depends on the specific model being used. Two main challenges when using multiple imaging modalities to study the pancreas are (1) the pancreas and surrounding abdominal anatomy have a deformable structure, making it difficult to consistently align the images and (2) breathing by the individual during image collection further complicates the alignment between multimodal images. Even after using state-of-the-art deformable image registration techniques, specifically designed to align abdominal images, multimodal images of the abdomen are often not perfectly aligned. We examine how the choice of different fusion points, ranging from early in the image processing pipeline to later stages, impacts the segmentation of the pancreas on imperfectly registered multimodal magnetic resonance (MR) images. Approach: Our dataset consists of 353 pairs of T2-weighted (T2w) and T1-weighted (T1w) abdominal MR images from 163 subjects with accompanying pancreas segmentation labels drawn mainly based on the T2w images. Because the T2w images were acquired in an interleaved manner across two breath holds and the T1w images on one breath hold, there were three different breath holds impacting the alignment of each pair of images. We used deeds, a state-of-the-art deformable abdominal image registration method to align the image pairs. Then, we trained a collection of basic UNets with different fusion points, spanning from early to late layers in the model, to assess how early through lat

关键词： Image segmentation

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Utilizing the Prophet Model for Time Series Based Stock Price Forecasting

Utilizing the Prophet Model for Time Series Based Stock Pric...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Janmejay Pant Hitesh Kumar Pant Priyanshi Kotlia Devendra Singh Vaibhav Kumar Sharma Jaishankar Bhatt Department of Computer Science and Engineering Graphic Era Hill University India Department of Management Studies Kumaun University Nainital Uttarakhand India Department of Computer Applications Amrapali group of institutes Haldwani Uttarakhand India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Time series analysis and forecasting play a crucial role due to their extensive application across various practical domains. Time series data represents a sequential order or collection of data points depicting the values of a variable at regular time intervals. The stock market is widely regarded as one of the most intricate financial systems, comprising numerous components or stocks, whose prices fluctuate significantly over time. Stock market forecasting aims to reveal the trends within the market as they evolve. All investors in the stock market strive to maximize returns on their investments while minimizing associated risks Due to the high sensitivity and rapid changes in stock markets, the primary objective of stock-trend prediction is to devise innovative approaches that can anticipate stocks leading to substantial profits. A statistical model capable of predicting Indian stock prices will be constructed using time series data from the Indian stock market. In our study, we utilize daily stock price data from a prominent company listed on the National Stock Exchange (NSE) of India.

关键词： Sensitivity Time series analysis Predictive models Market research Data models Stability analysis Planning Reliability Stock markets Forecasting

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The hybrid cryptography for enhancing the data security in fog computing 10

The hybrid cryptography for enhancing the data security in f...

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10th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2021

作者： Tripathi, Sakshi Tiwari, Rakesh Kumar Nigam, Rajesh Gupta, Neetesh Kumar Verma, Bhupendra Department of Computer Science and Engineering & Information Technology Technocrats Institutes of Technology RGPV Madhya Pradesh Bhopal India

ISBN: (纸本)9780738105239

Cloud computing is taking as exciting technology because of its economic behavior. The major issue in the cloud is its security because it is far from end-user a new thing applied science is developed called fog computing. Fog computing is the standard partition infrastructure between edge and cloud computing. Security is the main concern or large issue in the cloud as well as fog computing. To overcome this security issues different types of encryption techniques is used but security problem is still there. Here, in this work, a hybrid encryption approach is used. It is a combination of symmetric algorithms that is Chacha20poly1305, Fernet, Multifernet, AESCM, and AESGCM. It offers a higher degree of security than the existing algorithm. It is applied all over the data, All datasets are encrypted by this technique as well as decrypt by this hybrid approach. Consider all the parameters like decryption time, storage, correlation, and encryption time. The result shows how it is a better approach than the existing algorithm. © 2021 IEEE.

关键词： Cryptography

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Enhancing Prognostication in Colorectal Cancer with Integrated Machine Learning for Improved Survival Prediction

Enhancing Prognostication in Colorectal Cancer with Integrat...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： R Velmurugan K Suresh T Thilagaraj Meenakshi Sharma D. Saranya Department of Computer Science (PG) Kristu Jayanti College Autonomous Bangalore India Department of Computer Science Christ (Deemed to be University) Bangalore India Department of Artificial Intelligence and Machine Learning Moodlakatte Institute of Technology Kundapura Karnataka India Computer Science and Engineering Global Group of Institutes Amritsar Punjab Department of Information Technology Sri Eshwar College of Engineering Coimbatore

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Machine learning methods are recently used to predict patient survival in colorectal cancer using such models as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), VGG16, and Support Vector Machines (SVM). Taking advantage of a combination of CT, MRI scan images, and clinical records with drug recommendations, the study also checks to see how these models compare for distinguishing between patients in terms of their illness course-whether they are going to get better or worse over time. The results reveal VGG16 has better accuracy than CNN, RNN and SVM; as the highest-performing model tested, it also demonstrates superior precision, recall and F1-score. The research findings also validate these proposed models as they compare favorably with existing literature. This presents a promising proposition: a new, revolutionary approach to using artificial intelligence to boost prognostic accuracy.

关键词： Support vector machines Drugs Recurrent neural networks Accuracy Magnetic resonance imaging Computed tomography Machine learning Colorectal cancer Predictive models Convolutional neural networks

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Spatial-Temporal Analysis of Collective Emotional Resonance in China During Global Health Crisis 25

Spatial-Temporal Analysis of Collective Emotional Resonance ...

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34th ACM Web Conference, WWW 2025

作者： Zhang, Limiao Qi, Xinyang Ma, Haiping Gao, Jie Zhang, Xingyi Hu, Yanqing Jin, Yaochu Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei China Institutes of Physical Science and Information Technology Anhui University Hefei China School of Innovation and Entrepreneurship Shandong University Qingdao China School of Computer Science and Technology Anhui University Hefei China Department of Statistics and Data Science College of Science Southern University of Science and Technology Shenzhen China Trustworthy and General AI Lab School of Engineering Westlake University Hangzhou China

ISBN: (纸本)9798400712746

The 21st century has already witnessed so many outbreaks with pandemic potential, including SARS (2002), H1N1 (2009), MERS (2012), Ebola (2014), Zika virus (2015), and the COVID-19 pandemic (2019). Using 60 million geotagged Sina Weibo tweets covering over 20 million active accounts, we investigate the collective emotional dynamics on social media in the most recent global pandemic, i.e., COVID-19. This research features two highlights: (1) It focuses on the Chinese population located in the initial epicenter of the pandemic. (2) It examines the initial year after the pandemic outbreak, a critical period where emotions were most intense due to the uncertainty and rapid developments related to the crisis. Using cross-disciplinary methods, we reveal a positive connection between online emotional resonance and geographic proximity, demonstrating a direct mapping between virtual network distances and physical spatial embedding. We propose a percolation-based index to measure the nationwide emotional resonance level with which we illustrate the significant economic impact of the global health issue. Finally, we identify a leader-follower pattern in emotional resonance fluctuations based on time-lag emotion correlations, revealing that less active regions play a crucial role in leading and responding to emotional changes. In the face of long COVID and emerging global health crises, our analysis elucidates how collective emotional resonance evolves, providing potential directions for online opinion interventions during global shocks. © 2025 Copyright held by the owner/author(s).

关键词： Economic and social effects

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Enhancement of Two-Photon Fluorescence Microscopy Through Auxiliary Microspheres for Microscopic Observation 14

Enhancement of Two-Photon Fluorescence Microscopy Through Au...

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14th IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Zhang, Feng Jin, Zijian Jiang, Chaodi Yuan, Shuai Yang, Yongliang Wang, Xiaoduo School of Electrical & Control Engineering Shenyang Jianzhu University Liaoning Province Shenyang110168 China School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Province Shenyang110168 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9798331506056

At present, it is still a great challenge to improve the observation accuracy of two-photon fluorescence microscope. A new method that using auxiliary microspheres as movable components on the sample is proposed to solve this challenge in this study, it can improve high-resolution imaging. In experiments using two-photon fluorescence microscopy, the half-width height (FWHM) of red fluorescent nanoparticles with a diameter of 20 nm was observed to be 296 nm using a primitive microscope system. In this paper by combining barium titanate microspheres with two-photon fluorescence microscopy, the accuracy of the point spread function of the nanoparticles can be improved by a factor of about 1.65, which results in a significant reduction of their half-width height measurements to 179 nm. The research is of great significance to improve the resolution of two-photon fluorescence microscope. This progress is very valuable for studying biological tissues and cell structures, it provides a new impetus for the sustainable development in the microscopic imaging field. © 2024 IEEE.

关键词： Microspheres

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Protein-Folding Inspired Programmable Control System for Robot Swarms

Protein-Folding Inspired Programmable Control System for Rob...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Zhang, Feng Gu, Yuping Wang, Zebin Yuan, Shuai Yang, Yongliang School of Electrical and Control Engineering Shenyang Jianzhu University Liaoning Shenyang110168 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Liaoning Shenyang110106 China School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Shenyang110168 China

ISBN: (纸本)9781665481090

In swarm robotics, most of the control algorithms are inspired by the behaviors of animal swarms. The capability gap between robots and animals, however, hinders the reliability of these algorithms. Toward developing reliable algorithms of robot swarms, a promising solution is learning from the collective behaviors of low-intelligent natural swarms, such as cell collectives and molecular assemblies. Here, we proposed a programmable control system inspired by protein folding. Using our homemade RoboFold platform, we demonstrated a protein-folding inspired programmable control method for robot swarms. By programming and designing the sequence of a thread of protein-like robots, the developed system can mimic the formation of hierarchical structures in robot swarms as the three-level of protein structures. We quantitatively characterized the specific features of self-organized morphologies. The experimental results together demonstrated the feasibility of this method. To summarize, this work successfully translated the principles of protein-folding into programming methods to control the morphology of robot swarms. Considering that the protein folding problem is essentially a physicochemical problem, this system is not only beneficial for the control of swarm robots but also has the potential to develop into a tool for studying the protein folding problem. © 2024 IEEE.

关键词： Robot programming

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CD-Guide: A Dispatching and Charging Approach for Electric Taxicabs

CD-Guide: A Dispatching and Charging Approach for Electric T...

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作者： Yan, Li Shen, Haiying Kang, Liuwang Zhao, Juanjuan Zhang, Zhe Xu, Chengzhong Xi'an Jiaotong University School of Cyber Science and Engineering Shaanxi Xi'an710049 China University of Virginia Department of Computer Science CharlottesvilleVA22903 United States Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Xi'an Jiaotong University School of Computer Science Shaanxi Xi'an710049 China University of Macau School of Computer Science Macau China

Previous methods for passenger demand inference are unable to capture the effect of all possible random factors (e.g., accident and weather), hence resulting in insufficient accuracy. Moreover, due to the lack of charging optimization, existing taxicab dispatching methods cannot be applied to electric taxicabs directly. We propose CD-Guide, which provides Charging and Dispatching Guide for electric taxicabs based on customized selection and training of historical passenger demand data, multiobjective optimization, and reinforcement learning (RL). By analyzing a large-scale electric taxicab data set, we found that: 1) the histogram of passengers' origin buildings is effective in illustrating the suitability of historical data for learning;2) passenger demands in different regions vary a lot due to various random factors;and 3) charging time must be considered in dispatching electric taxicabs. We first develop a passenger demand inference model based on customized selection and training of suitable historical passenger demand data. Then, we develop two taxicab guidance methods that utilize multiobjective optimization and RL, respectively, to maximize the taxicab's likelihood of finding passengers, maximally prevent the taxicab from missing passengers due to charging, and, meanwhile, maintain the continuous service of the taxicab. Extensive experiments on real-world data sets demonstrate that compared with the state of the art, CD-Guide increases the total number of served passengers by 100%, and the minimum State-of-Charge of all taxicabs by 75% during all time slots. © 2014 IEEE.

关键词： Reinforcement learning

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PatchWorkPlot: simultaneous visualization of local alignments across multiple sequences

arXiv

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arXiv 2025年

作者： Pospelova, Maria Safonova, Yana Computer Science and Engineering Department Pennsylvania State University State CollegePA16802 United States Huck Institutes of Life Science Pennsylvania State University State CollegePA16802 United States

Motivation Revealing structural variations across sequences of closely related individuals or species is crucial for understanding their diversification mechanisms and roles. Results We developed PatchWorkPlot, a tool for visualization of pairwise alignments of multiple annotated sequences as dot plots combined into a single matrix. Availability and implementation PatchWorkPlot is implemented using Python 3 and is publicly available at GitHub: ***/yana-safonova/PatchWorkPlot. © 2025, CC BY.

关键词： Visualization

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PDMA: Probabilistic service migration approach for delay-aware and mobility-aware mobile edge computing

PDMA: Probabilistic service migration approach for delay-awa...

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作者： Xu, Minxian Zhou, Qiheng Wu, Huaming Lin, Weiwei Ye, Kejiang Xu, Chengzhong Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Center for Applied Mathematics Tianjin University Tianjin China School of Computer Science and Engineering South China University of Technology Guangzhou China State Key Lab of IOTSC Department of Computer Science University of Macau China

As a key technology in the 5G era, mobile edge computing (MEC) has developed rapidly in recent years. MEC aims to reduce the service delay of mobile users, while alleviating the processing pressure on the core network. MEC can be regarded as an extension of cloud computing on the user side, which can deploy edge servers and bring computing resources closer to mobile users, and provide more efficient interactions. However, due to the user's dynamic mobility, the distance between the user and the edge server will change dynamically, which may cause fluctuations in Quality of Service. Therefore, when a mobile user moves in the MEC environment, certain approaches are needed to schedule services deployed on the edge server to ensure the user experience. In this article, we model service scheduling in MEC scenarios and propose a delay-aware and mobility-aware service management approach based on concise probabilistic methods. This approach has low computational complexity and can effectively reduce service delay and migration costs. Furthermore, we conduct experiments by utilizing multiple realistic datasets and use iFogSim to evaluate the performance of the algorithm. The results show that our proposed approach can optimize the performance on service delay, with 8%–20% improvement and reduce the migration cost by more than 75% compared with baselines during the rush hours. © 2021 John Wiley & Sons Ltd.

关键词： Mobile edge computing

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