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Structurally Consistent MRI Colorization using Cross-Modal Fusion Learning

Structurally Consistent MRI Colorization using Cross-Modal F...

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ieee International symposium on Biomedical Imaging

作者： Mayuri Mathur Anav Chaudhary Saurabh Kumar Gupta Ojaswa Sharma Indraprastha Institute of Information Technology Delhi India Purdue University USA All India Institute of Medical Sciences India

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Medical image colorization enhances the interpretability of imaging data by providing clearer anatomical insights. We aim to transfer diverse anatomical colors from Cryosection data to MRI scans while preserving MRI structural details. Our novel architecture achieves structurally consistent color transfer by fusing Cryosection segmentation semantics into the colorization process. This enables stable organ-level colorization in MRI images without requiring precise registration between the input MRI and Cryosection data or segmentation of the MRI itself. Additionally, we introduce a feature compression-and-activation mechanism that captures global organ-level information, suppresses noise, and facilitates accurate and realistic organ-specific colorization of MRI scans. Comparative experiments with state-of-the-art techniques demonstrate significant visual improvements, confirming the effectiveness of our method.

关键词： visualization Image segmentation Three-dimensional displays Image coding Accuracy Image color analysis Magnetic resonance imaging Semantics Colored noise Biomedical imaging

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Attention Aware Continual Learning in Digital Pathology Contexts

Attention Aware Continual Learning in Digital Pathology Cont...

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ieee International symposium on Biomedical Imaging

作者： Arijit Patra Tapabrata Chakraborti Data & Translational Sciences UCB UK Dept of Med Phys & Biomed Eng UCL UK

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

There has been a recent strategic push globally to integrate digital pathology analysis into national health programs, which has been further encouraged by the emergence of large pathology foundation models which promise to make analysis of such data possible at scale in future. However, large scale clinical deployment of such systems remain a challenge in settings where datasets arrive over extended time, and there are requirements of adaptation to new classes of data over time. Also, in many clinical and pharmaceutical settings, retention of past data is often complicated by privacy issues, legal requirements, or storage constraints. Existing deep architectures are susceptible to decline in performance when trained on new tasks over time, in a phenomenon termed as catastrophic forgetting, addressing which is one of the key challenges towards enabling robust learning systems. This is particularly compounded in digital pathology as often new classes will have heterogeneous samples in limited numbers which need to be learned robustly in an explainable and trustworthy manner, as required by emerging legislation worldwide that call for AI safety and transparency in high risk applications like healthcare. We mitigate catastrophic forgetting by designing enhanced representation learning for histology image analysis, which not only improves continual learning performance under non-stationary data environments, but does so in an interpretable, and hence trustworthy manner.

关键词： Continuing education Learning systems Representation learning Histopathology Legislation Medical services Safety Reliability Biomedical imaging Pharmaceuticals

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Design of a Lightweight Privacy Communication Network System for Medium Enterprises

Design of a Lightweight Privacy Communication Network System...

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ieee Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Ruixiang Suo Wenxuan Qiao Xinyang Bai Yuyang Zhang Ping Dong School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

Medium and large enterprises need to exchange confidential information frequently. Traditional encryption techniques can only protect the security of communication data, but the privacy of the communicating entities' identity information cannot be effectively protected. Currently, common solutions include virtual private networks (VPNs) and single/multiple agent-based forwarding mechanisms. However, the egress nodes of VPNs are susceptible to attacks and monitoring; agents in single-agent forwarding mechanisms are easy targets for single points of failure and attacks. Medium and large enterprises urgently need a solution that ensures communication privacy while being cost-effective and easy to manage. Therefore, this paper proposes a lightweight privacy communication network system based on a multi-agent mechanism to realize the non-association of communication parties and provide transparent transmission services. In order to schedule and manage the whole system flexibly, this paper implements a remote service and a visualization service in a Directory Server. In addition, an intelligent multi-objective optimization analysis of the route selection algorithm is introduced to ensure the anti-tracking and high efficiency of data transmission; meanwhile, traffic obfuscation technology is used to prevent attackers from recognizing the communication content and the identities of both parties through the traffic pattern, which improves the transmission and management security of the system. Test results show that the system effectively protects information communication relationships in medium and large enterprises.

关键词： Privacy data privacy Technological innovation data visualization Traffic control Virtual private networks Internet Communication networks Protection Optimization

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EIGEN: Enabling Efficient 3DIC Interconnect with Heterogeneous Dual-Layer Network-on-Active-Interposer 31

EIGEN: Enabling Efficient 3DIC Interconnect with Heterogeneo...

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31st ieee International symposium on High Performance Computer Architecture, HPCA 2025

作者： Jia, Siyao Jiao, Bo Zhu, Haozhe Chen, Chixiao Liu, Qi Liu, Ming Frontier Institute of Chip and System Fudan University State Key Laboratory of Integrated Chips and Systems China

ISBN: (纸本)9798331506476

Chiplet-based 3DICs have emerged as modular solutions for large-scale, high-performance computing systems. However, unlike monolithic Network-on-Chips (NoCs), 3DICs using active interposers encounter difficulties in managing heterogeneous and high-load network traffic patterns. The emerging field of Networks-on-Active-Interposer (NOAI), independently designed from top dies' interconnects, is desired to address these challenges by supporting flexible topologies, low-latency memory access, and reduced traffic congestion. To satisfy the requirements, we propose a heterogeneous dual-layer interconnect architecture, EIGEN, for chiplet-interposer systems, along with a reinforcement learning (RL)-based routing framework, which can provide efficient and flexible communication for chiplet-based 3DICs. EIGEN features an application-aware switch-programable interconnection layer (AspLayer) and a dynamic packet-routing interconnection layer (DynLayer) on the interposer, respectively. To optimize inter-chiplet data communication, we also develop an RL-based path routing framework tailored to this dual-layer architecture. The RL framework's state space includes topology, network, and memory metrics, while the RL reward is set as the product of average packet latency and link utilization. The effectiveness of EIGEN is demonstrated through different applications where CPU, GPU, and AI accelerator chiplets are integrated via NOAI. Simulation results show that the proposed EIGEN achieves up to {6 7. 1 6%} latency reduction, {5 3. 8 9%} hops reduction and {1 1. 2 1%} runtime reduction compared to the state-of-the-art (SOTA) chiplets interconnect architectures. Furthermore, sensitivity analysis of network scaling shows that the EIGEN architecture and framework exhibit strong scalability, with latency reduction of {2 0. 9 6%} to {5 2. 7 7%} and hop reduction of {1 9. 7 2%} to {4 3. 4 1%} as the system scales from 4 × 4 to 32 × 32 chiplets. © 2025 ieee.

关键词： Sensitivity analysis

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Multi-Scale System Reliability analysis of Multi-Layer Network Infrastructures 71

Multi-Scale System Reliability Analysis of Multi-Layer Netwo...

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71st Annual Reliability and Maintainability symposium, RAMS 2025

作者： Huynh, Phat K. Hoang, Ca Nguyen, Duy Truong, Tien Le, Trung Q. Department of Industrial and Systems Engineering North Carolina A&T State University 1121 E. Market Street 419 McNair Hall GreensboroNC27411 United States Department of Industrial & Management Systems Engineering University of South Florida 4202 E. Fowler Avenue TampaFL33620 United States Faculty of Telecommunication Engineer Posts and Telecommunications Institute of Technology 122 Hoang Quoc Viet Cau Giay District Hanoi122000 Viet Nam

ISBN: (纸本)9798350367744

This study presents a novel framework that integrates dynamic multi-layer graph theory with cascading failure dynamics to analyze the resilience and reliability of multi-layer network infrastructures. These infrastructures include critical systems such as power grids, which are inherently susceptible to cascading failures due to their complex, interconnected structures. Our research introduces a sophisticated model that dynamically captures the multi-dimensional interactions of the systems across various scales. Each layer of the network, from physical components to control systems, is continually updated with real-time operational data and historical incident records, enhancing the model’s predictive accuracy and responsiveness to emerging threats. The core of our multi-scale system reliability analysis is the evaluation of Node-Centric and Edge-Centric Subgraph Reliability Metrics (N-SRM and E-SRM), examining both intra-layer and inter-layer dynamics. Through extensive simulations under two critical scenarios-cyberphysical attacks and extreme weather conditions-we quantitatively assessed the network's resilience. For cyber-physical attacks, rapid propagation of failures was observed, significantly impacting the network's operational integrity. The N-SRM values across the layers exhibited steep declines within a short duration, demonstrating pronounced susceptibility to these attacks. In contrast, the impact of extreme weather was characterized by a gradual decline in reliability, suggesting a robustness that allows for effective adaptation strategies over an extended period. In addition, the inter-layer connections, particularly in Scenario B, demonstrated higher resilience with slower rates of reliability decline, underscoring the structural integrity and operational capability of the network. © 2025 ieee.

关键词： large scale systems

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Software Tools and Evaluation Models for Visual Analytics: Trends, Challenges, and Future Directions

Software Tools and Evaluation Models for Visual Analytics: T...

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

作者： Lakumarapu, Srikanth Sudha, P. Ruba Pajila, P.J. Beslin Sasirekha, K. Vinoth, R. Siva Subramanian, R. Cvr College of Engineering Department of Cse Telangana India S.A Engineering College Dept of Cse India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept of Cse India R.M.D Engineering College Dept of Csbs India Rmk College of Engg and Tech Department of Cse India Rmk College of Engg and Tech Dept of Cse India

ISBN: (纸本)9798331521394

Visual analytics (VA) is a complex interdisciplinary field which applies data visualization and analysis methods in order to extract knowledge from Big data. This paper aims at presenting an overview of the most common software tools and assessment frameworks for VA with an emphasis on the open source, commercial and specific tools for BI, healthcare and scientific purposes. It discusses the issues that VA has concerning varied data types, their usability and how the system can be expanded to accommodate large data sets. The paper also considers recent trends in AI and machine learning, virtual and augmented reality, and synchronous shared environments. Privacy and data bias issues are also drawn out as ethical concerns. Last, the paper presents the directions of further research to enhance the efficiency and usability of VA tools and make them more ethical in data decision-making. © 2025 ieee.

关键词： Video analysis

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Ensemble-Driven Multilingual Sentiment analysis Framework for YouTube Comments with Dashboard

Ensemble-Driven Multilingual Sentiment Analysis Framework fo...

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

作者： Dharini, N. Madhuvanthi, M. Aswini, C.S. Lakshya, R. Triumbika, M. Saranya, N. Tiruvallur India R.M.K. College of Engineering and Technology Dept of Al & Ds Tiruvallur India

ISBN: (纸本)9798331521394

In the present-day digital age, sites like YouTube produce a vast volume of multilingual user comments. These comments are perfect for sentiment analysis because they contain a wealth of viewpoints and feelings. This article investigates how sentiments might be extracted from multilingual YouTube comments to improve user engagement and content comprehension. Our method divides sentiments into categories such as positive, negative, mixed feelings, and non-relevant using sophisticated natural language processing (NLP) techniques. This study illustrates the difficulties related to linguistic diversity and context sensitivity while also showcasing the efficacy of sentiment analysis in capturing the subtleties of user thoughts through the use of a large dataset of YouTube comments. The generated insights are meant to inform marketers and content creators about the preferences and opinions of their audiences in order to inform strategic decision-making. The development of an interactive dashboard will also facilitate the visualization of sentiment patterns in real-time, enabling stakeholders to engage with their audiences on a deeper level. This study demonstrates the potential of multilingual sentiment analysis as a tool for understanding global audience dynamics, developing the field, and enhancing the performance of automated content evaluation systems. © 2025 ieee.

关键词： Emotion Recognition

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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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Weakly Supervised Contrastive Learning for Better Severity Scoring of Lung Ultrasound

Weakly Supervised Contrastive Learning for Better Severity S...

引用

ieee International symposium on Biomedical Imaging

作者： Gautam Gare Hai Tran Bennett DeBoisblanc Ricardo Rodriguez John Galeotti Carnegie Mellon University Pittsburgh USA LSUHSC Internal Medicine New Orleans USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

With the onset of the COVID-19 pandemic, ultrasound has emerged as an effective tool for bedside monitoring of patients. Due to this, a large amount of lung ultrasound scans have been made available which can be used for AI based diagnosis and analysis. Several AI-based patient severity scoring models have been proposed that rely on scoring the appearance of the ultrasound scans. AI models are trained using ultrasound-appearance severity scores that are manually labeled based on standardized visual features. We address the challenge of labeling every ultrasound frame in the video clips. Our contrastive learning method treats the video clip severity labels as noisy weak severity labels for individual frames, thus requiring only video-level labels. We show that it performs better than the conventional cross-entropy loss based training. We combine frame severity predictions to come up with video severity predictions and show that the frame based model achieves comparable performance to a video based TSM model, on a large dataset combining public and private sources.

关键词： COVID-19 visualization Ultrasonic imaging Lungs Biological system modeling Contrastive learning Predictive models Noise measurement Labeling Artificial intelligence

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RDMP: A Reference Detection and Mask Propagation Pipeline for Numerous 3D Glomeruli Segmentation in large Volumes

RDMP: A Reference Detection and Mask Propagation Pipeline fo...

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ieee International symposium on Biomedical Imaging

作者： Ziling Lin Zehua Li Zifan Chen Yao Lu Fangxu Zhou Bin Dong Li Zhang Haifeng Li Center for Data Science Peking University Beijing China Peking University First Hospital Beijing China College of Future Technology Peking University Beijing China Beijing International Center for Mathematical Research Peking University Beijing China Center for Machine Learning Research Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Quantitative analysis of numerous repetitive structures in large volumes is one of the most challenging tasks in biomedical imaging, primarily due to its intensive manual requirements. Kidney glomeruli exemplify this challenge due to their dispersion across the cortical region, high density and microscopic size. To address this, we present a Reference Detection and Mask Propagation (RDMP) pipeline that combines object detection with 2D and 3D mask propagation, gradually achieving efficient segmentation of the entire volume by using a 2D reference example. Our method demonstrates robust performance, achieving a Dice Similarity Coefficient (DSC) of 0.9096 on microCT data, and when generalized to Synchrotron Radiation X-ray (SRX) data, reaching a DSC of 0.8431 with only minimal bounding box fine-tuning. Overall, this pipeline shows strong potential for efficient, large-scale kidney analysis, significantly reducing labor demands and accelerating kidney disease research.

关键词： Image segmentation Three-dimensional displays Statistical analysis Microscopy Pipelines Object detection Manuals Kidney Synchrotron radiation Biomedical imaging

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