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检索条件"任意字段=IEEE Symposium on Large Data Analysis and Visualization"

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Medxchat: A Unified Multimodal large Language Model Framework Towards CXRS Understanding and Generation

Medxchat: A Unified Multimodal Large Language Model Framewor...

引用

ieee International symposium on Biomedical Imaging

作者： Ling Yang Zhanyu Wang Zhenghao Chen Xinyu Liang Luping Zhou School of Electrical and Computer Engineering The University of Sydney Australia School of Information and Physical Sciences The University of Newcastle Australia The First Hospital Attached to Guangzhou University of Chinese Medicine

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Multimodal large Language Models (MLLMs) have shown promise in general image tasks, but their application in medical imaging remains limited. This study explores MLLMs for Chest X-Rays (CXRs) through MedXChat, a unified framework enabling seamless interaction for tasks such as text report generation, visual question-answering (VQA), and Text-to-CXR generation. Our MLLM, which uses natural language as input, breaks task boundaries and simplifies medical professional training to the maximum extent by allowing diverse tasks within a single environment. To understand CXRs, we employ visual encoders (e.g.,ViT) and LLMs (e.g., mPLUG-Owl), which we refine using visual adapters and delta-tuning. For CXR generation, we introduce an innovative synthesis approach that leverages the instruction-following capabilities of the Stable Diffusion (SD) architecture to produce fine-grained medical images without additional parameters. Through comprehensive experiments, our model demonstrates exceptional cross-task adaptability, showcasing proficiency across all three tasks.

关键词： Training visualization Adaptation models large language models Biological system modeling Natural languages data models X-ray imaging Biomedical imaging

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large-Scale Radiomics analysis for Prostate Cancer Detection Harnessing Machine and Deep Learning Models 22

Large-Scale Radiomics Analysis for Prostate Cancer Detection...

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22nd ieee International symposium on Biomedical Imaging, ISBI 2025

作者： Mylona, Eugenia Zaridis, Dimitrios I. Kalantzopoulos, Charalampos N. Pezoulas, Vasileios C. Tachos, Nikolaos S. Matsopoulos, George K. Regge, Daniele Papanikolaou, Nikolaos Tsiknakis, Manolis Marias, Kostas Fotiadis, Dimitrios I. Biomedical Research Institute Forth IoanninaGR 45110 Greece University of Ioannina Unit of Medical Technology Intelligent Information Systems Greece School of Electrical & Computer Engineering National Technical University of Athens Biomedical Engineering Laboratory Athens15780 Greece Candiolo Cancer Institute FPO-IRCCS Department of Radiology Strada Provinciale Candiolo Turin10060 Italy Champalimaud Foundation Computational Clinical Imaging Group Portugal Institute of Computer Science Forth Computational Biomedicine Laboratory HeraklionGR 70013 Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering HeraklionGR 71004 Greece

ISBN: (纸本)9798331520526

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability. © 2025 ieee.

关键词： Deep learning

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Preliminary Exploration of Oil-immersed Inverted Current Transformer Partial Discharge Identification by Wide Frequency Device

Preliminary Exploration of Oil-immersed Inverted Current Tra...

引用

Power Science and Technology (ICPST), ieee International Conference on

作者： Dun Qian Zhi Yang Yuzhe Feng Electric Power Research Institute State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

Oil-immersed inverted current transformers are widely used in power systems with voltage levels of 110kV and above. The insulation breakdown of oil-immersed inverted current transformer leads to a large explosion hazard. In order to solve this problem, deployed a wide frequency device for partial discharges based on the pulse current method. The article presents a preliminary exploration of partial discharge identification in oil-immersed inverted current transformers using a wide frequency device. The device employs the pulse current method to capture partial discharge data, which is then analyzed through cluster analysis, algorithmic judgment, and data visualization with manual judgment. The approach offers a promising tool for enhancing the safety and reliability of these critical electrical equipment.

关键词： Partial discharges Current transformers Electric breakdown Clustering algorithms data visualization Manuals Oil insulation

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Merits and Limits of Preregistration for visualization Research

Merits and Limits of Preregistration for Visualization Resea...

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Evaluation and Beyond - Methodological Approaches for visualization (BELIV)

作者： Lonni Besançon Brian A. Nosek Tamarinde Haven Miriah Meyer Cody Dunne Mohammad Ghoniem Linköping University Center for Open Science Inc. & University of Virginia Tilburg University Northeastern University Luxembourg Institute of Science and Technology

ISBN: (数字)9798331528461

ISBN: (纸本)9798331528478

The replication crisis has spawned a revolution in scientific methods, aimed at increasing the transparency, robustness, and reliability of scientific outcomes. In particular, the practice of preregistering study designs has shown important advantages. Preregistration can help limit questionable research practices, as well as increase the success rate of study replications. Many fields have now adopted preregistration as a default expectation for published studies. In 2022, we set up a panel “Merits and Limits of User Study Prereg-istration” with the overall goal of explaining the concept of prereg-istration to a wide VIS audience and discussing its suitability for visualization research. We report on the arguments and discussion of this panel in the hope that it can benefit the visualization com-munity at large. All materials and a copy of this paper are available on our OSF repository at https://***/wes57/.

关键词： Reviews Statistical analysis Interactive systems data visualization Robustness Open data

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Open-Source Cellular IoT Technologies Coverage data Collection System for Precision Agriculture

Open-Source Cellular IoT Technologies Coverage Data Collecti...

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ieee Latin American symposium on Circuits and Systems (LASCAS)

作者： Sebastián Maldonado Gonzalo Escuder Andrés Seré Leonardo Steinfeld Instituto de Ingeniería Eléctrica Facultad de Ingeniería Universidad de la República Montevideo Uruguay Mobile Network Operations Administración Nacional de Telecomunicaciones (ANTEL) Montevideo Uruguay

In remote rural areas, cellular IoT coverage remains limited and exhibits geographical variations, often leading to unreliable connectivity or even complete lack thereof. Mobile operators cannot afford comprehensive coverage assessments conducted through drive testing to provide coverage maps, hindering a wider adoption of IoT solutions for precision agriculture. However, system integrators and farmers could conduct on-demand field coverage assessments to evaluate the feasibility of embracing cellular as a Low Power Wide Area Network (LPWAN) communication technology in their production units if an affordable tool were accessible. This work proposes an open-source tool for cellular IoT coverage analysis. The system comprises a low-cost, portable, and autonomous device with a Global Navigation Satellite System (GNSS) chip and a cellular modem. Constructed using affordable, interconnectable kits (Arduino Zero and a Quectel BG96 modem board) powered by a power bank, it offers several hours of energy autonomy. The device collects network information and transmits it in georeferenced form via MQTT (Message Queuing Telemetry Transport) to a server for storage and user-friendly visualization. The server side is composed of four open-source systems. Eclipse Mosquitto serves as the system's core, implementing the MQTT protocol. Telegraf, a server-based agent, subscribes to Mosquitto topics, extracts data from JSON-formatted messages, and sends it to InfluxDB, a time-series database, for storage. Finally, Grafana serves as a powerful web-based data visualization system, retrieving and displaying InfluxDB data through a color-coded map. Experimental results demonstrate the exceptional potential and effectiveness of the proposed solution.

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Lightweight Contextual LLMs for IoT data Interpretation in Smart Cities

Lightweight Contextual LLMs for IoT Data Interpretation in S...

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2025 International Conference on Visual Analytics and data visualization, ICVADV 2025

作者： Kannadasan, Tamilarasan Independent Researcher TX United States

ISBN: (纸本)9798331521394

While the populations in cities are growing, the need for smart cities to optimize resources and life quality is increasingly a pressing matter. At the heart of this transformation lie the IoT devices creating massive volumes of data to be interpreted in real time. This paper introduces the Lightweight Contextual large Language Models designed for efficient processing of IoT data in Smart Cities. In the approach proposed, advanced model pruning and knowledge distillation are used, keeping the solution computationally efficient with no sacrifice in interpretative power. We validate our model's effectiveness on the UrbanSensing dataset in anomaly detection, predictive maintenance, and analytics of engagement on tasks involving citizens. Our framework allows feasibility on edge devices with a 40% reduction in inference time and up to 50% decrease in computational overhead, thus guaranteeing scalability and sustainability in real-world smart city applications. The findings bridge the gap from computational efficiency to contextual understandings, thereby offering the premise for enhanced urban resilience and wiser decision-making. © 2025 ieee.

关键词： Sentiment analysis

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Cinema Darkroom: A Deferred Rendering Framework for large-Scale datasets 10

Cinema Darkroom: A Deferred Rendering Framework for Large-Sc...

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10th ieee symposium on large data analysis and visualization (LDAV)

作者： Lukasczyk, Jonas Garth, Christoph Larsen, Matthew Engelke, Wito Hotz, Ingrid Rogers, David Ahrens, James Maciejewski, Ross Arizona State Univ Tempe AZ 85287 USA TU Kaiserslautern Kaiserslautern Germany Lawrence Livermore Natl Lab Livermore CA 94550 USA Linkoping Univ Linkoping Sweden Los Alamos Natl Lab Los Alamos NM USA

ISBN: (纸本)9781728184685

This paper presents a framework that fully leverages the advantages of a deferred rendering approach for the interactive visualization of large-scale datasets. Geometry buffers (G-Buffers) are generated and stored in situ, and shading is performed post hoc in an interactive image-based rendering front end. This decoupled framework has two major advantages. First, the G-Buffers only need to be computed and stored once-which corresponds to the most expensive part of the rendering pipeline. Second, the stored G-Buffers can later be consumed in an image-based rendering front end that enables users to interactively adjust various visualization parameters-such as the applied color map or the strength of ambient occlusion-where suitable choices are often not known a priori. This paper demonstrates the use of Cinema Darkroom on several real-world datasets, highlighting CD's ability to effectively decouple the complexity and size of the dataset from its visualization.

关键词： Deferred Rendering Image databases In Situ visualization Post Hoc analysis Image-Based Shading

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Explaining noise effects in CNN: a practical case study on volcano signals 35

Explaining noise effects in CNN: a practical case study on v...

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35th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)

作者： Canario, Joao Paulo Ribeiro, Otavio Rios, Ricardo Univ Fed Bahia Inst Comp Salvador Brazil

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

The study presented in this work was devoted to understanding the effect of noises in classifiers created to model signals, a particular case of data analysis characterized for presenting temporal dependencies. In this sense, we have considered a real-world application related to predicting seismic volcano events based on its temporal monitoring. Firstly, we created a convolutional neural network (CNN) to model and predict such signals. Then, we have used statistical tests to confirm the noise influence on our network. Finally, we have used an eXplainable AI (XAI) approach to describe the noise behavior visually. Besides developing a CNN with an outstanding performance to deal with this real-world application, the theoretical contribution of our work is twofold: firstly, the adoption of an XAI approach, originally designed to explain image classification, to describe the influence of noise in signals;and, secondly, we show an approach to reconstruct signals after this explaining process to smooth their observations in the time domain.

关键词： visualization Time-frequency analysis Predictive models Volcanoes Safety Convolutional neural networks Time-domain analysis

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data+Shift: Supporting visual investigation of data distribution shifts by data scientists

Data+Shift: Supporting visual investigation of data distribu...

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24th Eurographics Conference on visualization, EuroVis 2022

作者： Palmeiro, João Malveiro, Beatriz Costa, Rita Polido, David Moreira, Ricardo Bizarro, Pedro Feedzai

ISBN: (纸本)9783038681847

Machine learning on data streams is increasingly more present in multiple domains. However, there is often data distribution shift that can lead machine learning models to make incorrect decisions. While there are automatic methods to detect when drift is happening, human analysis, often by data scientists, is essential to diagnose the causes of the problem and adjust the system. We propose data+Shift, a visual analytics tool to support data scientists in the task of investigating the underlying factors of shift in data features in the context of fraud detection. Design requirements were derived from interviews with data scientists. data+Shift is integrated with JupyterLab and can be used alongside other data science tools. We validated our approach with a think-aloud experiment where a data scientist used the tool for a fraud detection use case. © 2022 The Author(s) © 2022 The Eurographics Association.

关键词： Machine learning

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Deep Learning-Based Semantic Segmentation in Autonomous Driving 23

Deep Learning-Based Semantic Segmentation in Autonomous Driv...

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23rd ieee International Conference on High Performance Computing and Communications, 7th ieee International Conference on data Science and Systems, 19th ieee International Conference on Smart City and 7th ieee International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Jebamikyous, Hrag-Harout Kashef, Rasha Ryerson University Dept of Electrical and Computer Engineering Toronto Canada

ISBN: (纸本)9781665494571

Perception is the first and most important task of any autonomous driving system. It extracts visual information about the surrounding environment of the vehicle. The perception data is then fed to a decision-making system to provide the optimum decision given a specific scenario to avoid potential collisions. In this paper, we have developed variants of the U-Net model to perform semantic segmentation on urban scene images to understand the surroundings of an autonomous vehicle. The U-N et model and its variants are adopted for semantic segmentation in this project to account for the power of the UNet in handling large and small datasets. We have also compared the best-performing variant with other commonly used semantic segmentation models. The comparative analysis was performed using three well-known models, including FCN-16, FCN-8, and SegNet. After conducting sensitivity and comparative analysis, it is concluded that the U-Net variants performed the best in terms of the Intersection over Union (IoU) evaluation metric and other quality metrics. © 2021 ieee.

关键词： Image segmentation visualization Analytical models Sensitivity Smart cities Semantics Decision making

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