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检索条件"任意字段=International Conference on Computer Graphics, Imaging and Visualization"

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MRI Image-Based Brain Tumor Classification Using Transfer Learning and XAI 3rd

MRI Image-Based Brain Tumor Classification Using Transfer Le...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Rayhan, Masum Mondal, Saykat Pinki, Farhana Tazmim Computer Science and Engineering Discipline Khulna University Sher-E-Bangla Rd Khulna9208 Bangladesh

ISBN: (纸本)9789819739363

Brain tumors, particularly those of the highest grade, can substantially lower life expectancy. A precise diagnosis is required for individuals with brain tumors to receive the best possible care. The conventional way of diagnosing brain tumors is Magnetic Resonance imaging (MRI) while recent tumor detection models leverage Convolution Neural Networks (CNN) and pre-trained models, many prioritize accuracy over providing explanation and interpretability. This paper addresses these shortcomings by proposing an innovative approach to the categorization of brain tumors by exploring transfer learning and Explainable Artificial Intelligence (XAI). The study uses the top five pre-trained models to classify three different types of tumors: VGG16, ResNet50, Xception, InceptionV3 and DenseNet201. Notably, a customized VGG16 model obtains a promising test accuracy of 99.04%, which is comparable to top literature scores. A significant contribution of this work is the integration of the top three explainable AI tools: Gradient-weighted Class Activation Mapping (Grad-CAM), Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). These tools enhance visualization and provide valuable explanations for model predictions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Magnetic resonance imaging

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Integrating Deep Learning Frameworks for Automated Medical Image Diagnosis 8th

Integrating Deep Learning Frameworks for Automated Medical I...

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8th IFIP TC 12 international conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Gayatri, K. Kumar, K. M. Anand Padmavathi, B. Shankar Department of Computer Science and Engineering Easwari Engineering College Ramapuram Chennai600089 India

ISBN: (纸本)9783031736162

In today’s healthcare system, medical image analysis is essential for diagnosis, treatment planning, and condition monitoring. In this study, the four well-known tools in the field are 3D Slicer, MONAI, SAMM, and YOLOv8 are examined and contrasted. The study’s scope includes its ability to process a variety of 2D and 4D medical imaging datasets, which contain images, segmentation, annotations, and transformations. The process entails a comprehensive prehensive examination of every tool’s feature, with particular emphasis on their capacity to read and write DICOM images, accommodate multiple file formats, offer interactive 3D Slicer. Key findings show that 3D Slicer performs exceptionally well when using deep learning techniques for segmentation and interactive visualization. While SAM Model exhibits adaptability in managing diverse segmentation prompts, MONAI provides a comprehensive end-to-end medical data processing solution. YOLOv8 shows potential for effective object detection. The study’s implications include the possibility of using these instruments in clinical practices and medical research contexts. The research results provide insight into the current discussion about how to strategically integrate state-of-the-art methods to improve overall healthcare outcomes, diagnostic accuracy, and treatment planning efficiency as medical image analysis continues to advance. © IFIP international Federation for Information Processing 2025.

关键词： Clinical research

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Dual-Structural Representation Learning with Attention-Based Graph Fusion for Hierarchical MCI-AD Diagnosis 17th

Dual-Structural Representation Learning with Attention-Based...

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17th international conference on Brain Informatics, BI 2024

作者： Cao, Yu Kuai, Hongzhi Wang, Bixin Fan, Fengmei Peng, Guanqiao Zhong, Ning Faculty of Information Technology Beijing University of Technology Beijing China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China Faculty of Engineering Maebashi Institute of Technology Gunma Maebashi Japan Peking University Huilongguan Clinical Medical School Beijing Huilongguan Hospital Beijing China College of Liberal Arts and Sciences University of Illinois Urbana-Champaign Illinois United States

ISBN: (纸本)9789819632930

The integration of AI, big data, and magnetic resonance imaging (MRI) has significantly advanced healthcare, particularly in the early diagnosis and treatment of neurodegenerative diseases. Alzheimer’s Disease (AD), which predominantly affects the elderly, and its precursor, Mild Cognitive Impairment (MCI), present considerable challenges in early detection due to the complex structural changes in the brain. Traditional diagnostic methods often struggle to capture these intricate changes. To address this, the systematic Brain Informatics methodology is reconsidered to realize evidence combination based on dual-structural representation and fusion computing via attention-based graph. In particular, the DS-AGF (Dual-Structural Representation Learning with Attention-Based Graph Fusion) model was proposed for hierarchical MCI-AD diagnosis. This model employs a dense convolution unit and a node-optimized convolution unit to learn multi-level, fine-grained brain representations. Additionally, an attention-based graph representation fusion unit is introduced, enabling the integration of these representations and allowing the model to capture both local and global relationships between brain regions. This approach enhances the network feature learning ability on critical features, enabling multi-view visualization of key brain regions and their connectivity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Magnetic resonance imaging

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In-situ Ultrasound Guided Closed Long Bone Shaft Fracture Treatments

In-situ Ultrasound Guided Closed Long Bone Shaft Fracture Tr...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE conference on

作者： Wenqing Yan Haowei Li Shiye Huang Long Qian Hui Ding Guangzhi Wang Zhe Zhao Tsinghua University Beijing Tsinghua Changgung Hospital Medivis Inc.

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

Closed Reduction and Internal Fixation (CRIF) is the preferred treatment for closed long bone shaft fractures, where frequent X-rays imaging reduce efficiency and increase radiation exposure. Intra-operative ultrasound offers real-time, radiation-free bone imaging, but challenges orthopedic surgeons. To address this, we developed an Augmented Reality (AR) navigation system with in-situ ultrasound visualization, combining on-device infrared tool tracking via an AR headset’s depth sensor with remote rendering for accurate, low-latency images. A standard operating procedure for surgical application was also proposed. Clinical trials with six patients showed significant reduction in fluoroscopy dose and operation time.

关键词： Shafts Ultrasonic imaging Navigation Surgery X-rays Bones Rendering (computer graphics) Standards Augmented reality X-ray imaging

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Attention-Based Multi-Scale Fusion for Brain Tumor Classification with Explainable AI

Attention-Based Multi-Scale Fusion for Brain Tumor Classific...

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international conference on Electrical, computer and Communication Engineering (ECCE)

作者： Md. Asraful Sharker Nirob Prayma Bishshash Tania Khatun Shayla Sharmin Md Zahid Hasan Mohammad Shamsul Arefin Department of Computer Science & Engineering Daffodil International University Dhaka Bangladesh Division of Computer Science and Engineering Louisiana State University Baton Rouge Louisiana USA Department of Computer Science & Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Accurate classification of brain tumors from MRI scans is crucial for effective diagnosis and treatment planning in neuro-oncology. This study presents a comprehensive framework leveraging advanced deep-learning techniques and visualization methods to achieve precise tumor classification. Our methodology involves merging two diverse datasets, performing data augmentation, and standardizing image dimensions to facilitate robust model training. We propose an attention-based multiscale fusion model, which integrates spatial attention and multi-scale fusion layers, achieving an accuracy of 99.17%, surpassing other models such as DenseNet201 (87.21%), InceptionV3 (82.30%), and MobileNet V3 (92.01%). Advanced visualization techniques, including Grad-CAM, enhance interpretability and confidence in diagnostic assessments. Moving forward, future work could explore integrating multi-modal imaging data and addressing challenges related to data scarcity and class imbalances to enhance diagnostic accuracy further and personalize treatment recommendations. Collaboration between computer scientists and clinicians is essential for seamless integration of AI systems into clinical workflows, ensuring reliable and safe deployment in real-world settings.

关键词： Training Accuracy Explainable AI Magnetic resonance imaging Brain tumors Data visualization Collaboration Brain modeling Data augmentation Data models

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Semi-Mamba: Improving Medical Image Segmentation via Semi-Automatic Mamba Network 41st

Semi-Mamba: Improving Medical Image Segmentation via Semi-Au...

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41st computer graphics international conference, CGI 2024

作者： Bi, Lei Peng, Yige Feng, David Dagan Kim, Jinman Institute of Translational Medicine National Center for Translational Medicine Shanghai Jiao Tong University Shanghai China School of Computer Science University of Sydney CamperdownNSW Australia

ISBN: (纸本)9783031820236

Segmentation of regions of interest (ROIs) from medical images is considered as a fundamental requirement for many imaging-based clinical decision support systems. Deep learning based automatic segmentation methods have emerged as the state-of-the-art. However, their performance is heavily reliant on using a large number of annotated training dataset to encompass all the possible variations of ROIs, and when the coverage of these variations is inadequate, these methods have difficulties in segmenting the ROIs. Semi-automatic segmentation methods, which fuse user-inputs with high-level semantic image features derived from convolutional neural networks (CNNs) or vision transformers (ViTs) offer an alternative to overcome the limitations of automatic segmentation methods. Unfortunately, CNNs are restricted by the limited receptive fields that cannot capture long-range dependencies, while ViT usually rely on the self-attention mechanism to capture the global context and therefore have high computational complexity. In this study, we propose a Semi-Mamba method for semi-automatic medical image segmentation. The novelty we introduce is to leverage the capability of Mamba to efficiently capture long-range dependency and combine this together with user-clicks to segment challenging ROIs, e.g., ROIs with fuzzy boundaries and inhomogeneous textures. Our experiments with three well benchmarked medical image datasets across different modalities showed that our method consistently outperformed existing automatic and semi-automatic segmentation, which demonstrates strong generalizability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Architectural visualization using Augmented Reality

Architectural Visualization using Augmented Reality

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), international conference

作者： Nikhil Raj Hema P Menon Abhinand Satheesh Naveen Nikhil A. P Vishnuraj K. R Department of Computer Science and Engineering Sreepathy Institute of Management and Technology Vavannor Palakkad Kerala

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

In the dynamic field of architectural design, effective communication stands as a requirement for successful project realization. However, traditional 2D methods often struggle to convey the depth and essence of architectural visions to stakeholders. Augmented Reality (AR) emerges as a revolutionary tool, poised to transform architectural communication and stakeholder engagement. This work is an exploration of the transformative power of Augmented Reality in architectural design communication, employing a comprehensive methodology. By leveraging state-of-the-art technological innovations, the aim is to provide architects and designers with groundbreaking tools to translate their blueprints into immersive, dynamic experiences. Beginning with collaborative requirement analysis, conceptual design, and data acquisition, the optimization of assets for AR rendering follows suit. Augmented Reality, in tandem with Unity 3D, Figma, and specifically smartphones as AR devices are used for realization of the work. This integration fosters collaborative, immersive design experiences and propels architectural discourse into a new era of interactive visualization and communication.

关键词： Technological innovation visualization Three-dimensional displays Collaboration Transforms Rendering (computer graphics) Stakeholders Augmented reality Standards Smart phones

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TextHair3D: Text-driven 3D Hair Editing with Generative Priors

TextHair3D: Text-driven 3D Hair Editing with Generative Prio...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaoxue Li Huilong Pi Yunchuan Qin Ruihui Li Kenli Li College of Computer Science and Electronic Engineering Hunan University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Text-driven hair editing on 3D heads is a challenging problem in computer vision and graphics. In this paper, we propose TextHair3D, a NeRF-based text-driven 3D hair editing method that uses 3D perception to generate priors, edit hair attributes from user-provided text, and preserve facial features. TextHair3D uses the Contrastive Language-Image Pre-training (CLIP) model to encode textual conditions. To address the complexity and roughness of local editing, we design a combined conditional mapping module to map image and text conditions into latent space for learning generative priors. This enables high-quality, photo-realistic hair editing and 3D head reproduction. Extensive experiments show Tex-tHair3D’s superiority in visual realism and attribute accuracy.

关键词： Hair visualization Three-dimensional displays Head Space mapping Signal processing Rendering (computer graphics) Neural radiance field Speech processing Facial features

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Design and Evaluation of a Serious Game on Household Sustainability 13th

Design and Evaluation of a Serious Game on Household Sus...

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13th international conference on Games and Learning Alliance, GALA 2024

作者： Sau, Jan Boada, Imma Josa, Aleix Dorca Sánchez-Pascuala Gómez, Laura Universitat d’Andorra Sant Julià de LòriaAD600 Andorra Graphics and Imaging Laboratory Universitat de Girona Catalonia Girona17003 Spain

ISBN: (纸本)9783031782688

Sustainability education has evolved from a necessity to a responsibility, or even an obligation. Numerous awareness initiatives have been implemented to meet this, including advertising campaigns and educational materials. Serious games combine entertainment with education and offer a powerful communication strategy to engage society and promote sustainability awareness. This paper presents Pick Energy Cards, a prototype of a serious game designed to educate players on energy consumption, water waste, and environmental pollution. The study’s primary focus is the evaluation process, which aims to validate the game concept and assess both its usability and playability before testing its educational impact. To achieve this, the EGameFlow usability survey has been employed. A diverse group of 24 participants, aged 18 to 60, with varying levels of video game experience, participated in the study. After playing the game, participants completed the survey. The results demonstrate that Pick Energy Cards has the potential to serve as an effective learning tool for players across all age groups. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy Evaluation Gamification Household ecology Serious games Sustainability

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2nd international conference on Applied Algorithms, ICAA 2025

2nd International Conference on Applied Algorithms, ICAA 202...

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2nd international conference on Applied Algorithms, ICAA 2025

ISBN: (纸本)9783031845420

The proceedings contain 29 papers. The special focus in this conference is on Applied Algorithms. The topics include: On the Characterization of Eulerian es-Splitting p-Matroids;approximability of Edge-Vertex Domination in Unit Disk Graphs;density Extrema of Integer Points in Standard Hexagons;Dilution with Digital Microfluidic Biochips: Unbalanced Split-Error Correction with SIMOP;social Evolution of Published Text and the Emergence of Artificial Intelligence Through Large Language Models and the Problem of Toxicity and Bias;HITgram: A Platform for Experimenting with n-Gram Language Models;Overlapping Community Detection Using Dynamic Residual Deep GCN;DeepUIR-Net: Underwater Image Restoration Using Residual-UNet with Optimized Efficient Channel Attention Network Integration;deepfake Image Detection Using Light-Weight Attention Integrated MobileNetV3 Model;AI-Driven Monitoring System for Detecting People Using Mobile Phones in Restricted Zone;integrated Analysis of Voice Patterns and Semantic Features for Emotion Recognition;Using Various Machine Learning Algorithms and xLSTM-UNet for Crop Recommendation and Disease Prediction;he-Li Graph Convolutional Neural Network: Deep and Enhanced Adaptation of Interest Modeling for Personalized Recommendation System;creation of Cartoon Face Images of Celebrities: Automation;deployable Solution for Real-Time Children Face Emotion Prediction System;vision Transformers in Evaluating Bread Edibility;MobileNet Based Fruit Classification Using UNet Generated Segmented Images: GRAD-CAM visualization;RExAS: Relation Extraction Using Adaptive Self-attention;Kontho: An AI-Driven Smart Glove for Enhanced Sign Language Communication;leveraging EfficientNetB4 Model with Multi-head Attentions for Maize Leaf Disease Detection;automated Module for Image Quality Assessment from Narrow-Banding imaging Endoscopy Cameras;does Varied Developer Interactions Cause Bugs to Be Resolved Faster? A Study of Open Source Software Ecosystems;movie Recommen

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