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Predicting local fMRI activations from EEG: a Feasibility Study Using Both Classical and Modern Machine Learning Pipelines

Predicting local fMRI activations from EEG: a Feasibility St...

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

作者： Tomer Amit Taly Markovits Guy Gurevitch Talma Hendler Lior Wolf The School of Computer Science Tel-Aviv University Tel Aviv Israel Sagol School of Neuroscience Tel Aviv University Tel Aviv Israel

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

fMRI’s clinical use is limited by cost, while EEG is more accessible but lacks spatial detail and deep brain coverage. Research aims to predict deep brain activations from combined fMRI and EEG data. We compare classical machine learning and a CNN-transformer pipeline for this mapping across multiple brain regions. As we show, in the first dataset, which is heavily tilted toward visual perception, the activations in the Hippocampus cannot be recovered reliably from EEG, using either pipeline. However, in other regions, predictability is much higher, and in those cases, the deep learning pipeline obtains better predictions. In a second dataset that is based on musical feedback while the visual is blocked, both pipelines yield improved results in the Hippocampus.

关键词： Deep learning visualization Pipelines Time series analysis Functional magnetic resonance imaging Electroencephalography Reliability Speech processing Hippocampus Visual perception

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Object-level Data Augmentation for Visual 3D Object Detection in Autonomous Driving

Object-level Data Augmentation for Visual 3D Object Detectio...

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

作者： Haoran Cheng Junkai Xu Liang Peng Zheng Yang Xiaofei He Boxi Wu Zhejiang University Hangzhou China FABU Inc Hangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Data augmentation plays an important role in visual-based 3D object detection. Existing detectors typically employ image/BEV-level data augmentation techniques, failing to utilize flexible object-level augmentations because of 2D-3D inconsistencies. This limitation hinders us from increasing the diversity of training data. To alleviate this issue, we propose an object-level data augmentation approach that incorporates scene reconstruction and neural scene rendering. Specifically, we reconstruct the scene and objects by extracting image features from sequences and aligning them with associated LiDAR point clouds. This approach is intended to conduct the editing process within a 3D space, allowing for flexible object manipulation. Additionally, we introduce a neural scene renderer to project the edited 3D scene onto a specified camera plane and render it onto a 2D image. Combined with scene reconstruction, it overcomes the challenges stemming from 2D/3D inconsistencies, enabling the generation of object-level augmented images with corresponding labels for model training. To validate the proposed method, we apply our method to various multi-camera 3D object detectors, consistently boosting the performance.

关键词： visualization Three-dimensional displays Laser radar Training data Detectors Object detection Data augmentation Rendering (computer graphics) Feature extraction Image reconstruction

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Visual Decoding Using a Learnable Wavelet-Based Spatial-Spectral-Temporal EEG Embedding

Visual Decoding Using a Learnable Wavelet-Based Spatial-Spec...

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international Winter Workshop on Brain-computer Interface (BCI)

作者： Yebin Choi Jun-Mo Kim WooHyeok Choi Chang-Hoon Ji Ji-Hye Oh Tae-Eui Kam Dept. Human-Centered Artificial Intelligence Sangmyung University Seoul South Korea Dept. Artificial Intelligence Korea University Seoul South Korea

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

Visual decoding seeks to identify or reconstruct visual stimuli perceived by individuals based on neural activity. Although functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) have achieved remarkable success in visual decoding, their high costs, limited portability, and real-time processing challenges necessitate alternative approaches. Electroencephalography (EEG) provides a promising solution due to its cost-effectiveness, high temporal resolution, and suitability for real-time applications. However, conventional EEG-based encoders often rely on simplistic architectures, which limits their ability to fully capture the spatial, spectral, and temporal (SST) features of EEG signals, leading to suboptimal performance. In this study, we propose a novel brain decoding framework utilizing a state-of-the-art EEG encoder specifically designed to capture the SST characteristics of EEG signals. The proposed framework was evaluated on the THINGS-EEG dataset. It achieved a mean top-l accuracy of 19.7% and a top-5 accuracy of 50.7% in zero-shot retrieval tasks, outperforming conventional EEG encoders. These results demonstrate the potential of our method in advancing EEG-based visual decoding task.

关键词： visualization Accuracy Functional magnetic resonance imaging Feature extraction Electroencephalography Real-time systems Decoding Spatial resolution Image reconstruction Visual perception

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Comprehensive Assistive Language Model (CALM) for Neurodivergent Child Language Acquisition

Comprehensive Assistive Language Model (CALM) for Neurodiver...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), international conference on

作者： Shivranjani Balaji S. Vigneshwari Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The Comprehensive Assistance Language Model–CALM, is a Natural Language Processing framework designed to support consistent language acquisition in children and aid their phonetic, semantic, and syntactic understanding. The model is intentionally designed to engage the child through empathy, task simplification, age-appropriate adaptation and trait-based multimodality. Most neurodivergent children struggle with language acquisition through traditional learning methods and develop a language gap that continues to grow into their adolescence and adulthood. Timely intervention through immersive learning approaches can mitigate or prevent these challenges. CALM integrates two components-a prompt-tuned LLM and a trained transformer model. The inclusion of a user report prepared by the prompt-tuned LLM on traits, age, caretaker concerns and detected condition understanding, increases the response quality and the child’s learning engagement. Layering a transformer model for contextual refinement and grammar correction over the LLM-generated response ensures coherence and improves relevance in answering the child’s query. CALM uses emojis and graphics as part of the generated text to enhance visual learning and also has the option of speech input/output through the incorporated LLM. CALM has achieved meaningful results when measured using evaluation metrics such as accuracy and response time, as well as manually validated characteristics including empathy, bias, engagement, and relevance. CALM effectively simplifies tasks, visualises learning and aligns solutions to the child’s interests.

关键词： Adaptation models visualization Accuracy Phonetics Transformers Natural language processing Time measurement Time factors Usability Tuning

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Application of Visual Communication in the Three-Dimensional Image Design of Interior Architecture

Application of Visual Communication in the Three-Dimensional...

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Intelligent Systems and Computational Networks (ICISCN), international conference on

作者： Danqiong Liu Shengke Zhou Qian Sun Yuncheng Vocational and Technical University Yuncheng China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

This study explores the application of visual communication design in interior architectural three-dimensional image design, aiming to solve the problems of unclear definition of indoor LoD, low modeling efficiency and insufficient integration of indoor space information in smart city construction. The research method is based on visual communication design, which enhances the expressiveness and user experience of interior design through the integration of visual elements such as graphics, color and typography. First, this study introduces the application of visual communication design in automated modeling, using visual recognition technology to automatically generate three-dimensional models from floor plans, which significantly improves modeling efficiency. Secondly, the application of visual communication design in indoor navigation and positioning services is studied to provide more intuitive navigation instructions through augmented reality technology, thereby improving the accuracy and user experience of indoor navigation. Experimental results show that the visual communication method performs well in LoD evaluation under different view conditions, with significant advantages over traditional methods in terms of geometric accuracy, texture quality, element integrity, and indoor LoD similarity. In addition, the application of visual communication improves the efficiency of indoor 3D image design and improves the integration of indoor space information. The evaluation of the rationality of element layout reaches 94.15%, providing a more effective indoor architectural 3D image design solution for smart city construction. plan.

关键词： Solid modeling visualization Three-dimensional displays Accuracy Visual communication Smart cities Indoor navigation computer architecture User experience Parametric statistics

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AI-Powered MRI Scan Analysis for Enhanced Patient Hospitality: Real-Time Report Generation for Faster Diagnosis - A Systematic Review

AI-Powered MRI Scan Analysis for Enhanced Patient Hospitalit...

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Visual Analytics and Data visualization (ICVADV), international conference on

作者： M. Jaganath G. Ramya Shri B. I. Bharani S. G. Manoj Kumar S. Saravana Haris B. Karneya Department of Artificial Intelligence and Machine Learning M.Kumarasamy College of Engineering Karur India Department of Computer Science and Engineering M.Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

MRI scans play a vital role in the diagnosis and treatment of numerous medical conditions, and efficient analysis of these scans using AI is necessary for enhancing patient outcomes. This systematic review explores the application of deep learning models to AI-based MRI scan analysis, with an emphasis on real-time report generation for improving patient hospitality and speeding up the diagnosis process. We compare a range of deep learning networks such as ShuffleNet, VGG, EfficientNet, and AlexNet for examining the accurate model for MRI scan data classification. The results indicate that AlexNet performs better than the rest of the models, registering a remarkable 98% accuracy, beating VGG (96%), EfficientNet, and ShuffleNet (both 97%). AlexNet presents better performance in classifying critical classes like Elbow Mucosa, Elbow Crack, and Knee Benign Tissue with ideal precision, recall, and F1-scores. Yet, it indicates somewhat poor performance for Knee Muscularis and Knee Serosa. The results indicate that AlexNet provides a very effective tool for real-time automatic MRI scan analysis, leading to quicker diagnosis and better patient care. This AI-driven method, when incorporated into clinical processes, can really enhance diagnostic productivity and patient care.

关键词： Deep learning Analytical models Accuracy Magnetic resonance imaging Visual analytics Real-time systems Data models Medical diagnosis Elbow Systematic literature review

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Telemedicine Platforms with Cloud-Based AI-Driven Diagnostics in Hospitals

Telemedicine Platforms with Cloud-Based AI-Driven Diagnostic...

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2025 international conference on Visual Analytics and Data visualization, ICVADV 2025

作者： Thangalakshmi, S. Mohankumar, N. Jayanthi, L.N. Blessy, Y.M. Meenakshi, R. Rajmohan, M. School of Marine Engineering and Technology Indian Maritime University Faculty - Electrical Tamil Nadu Chennai India Pune India Saveetha University Department of Commerce Tamil Nadu Chennai India R.M.K. Engineering College Department of Electronics and Communication and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331521394

Hospitals may benefit greatly from telemedicine technologies, including cloud-based artificial intelligence (AI)-driven diagnostics. Regardless of location, instantaneous access to medical knowledge is made possible by these systems that allow for virtual consultations between patients and healthcare practitioners. Critical patient data is always available during consultations due to these systems' use of cloud computing to safely store and handle massive volumes of data. The use of AI-powered diagnostics improves the precision and effectiveness of diagnoses. To help healthcare providers make informed decisions, machine learning algorithms examine patient data, including medical imaging, test findings, and clinical notes. Faster and more accurate diagnoses are possible with the help of this technology since it can spot patterns and abnormalities that human practitioners may miss. Hospitals can streamline their workflow using telemedicine technologies that use AI for diagnoses. They improve the efficiency of patient triage, the prioritization of critical situations, and the allocation of resources. Better healthcare delivery, shorter wait times, and improved patient outcomes are the effects of this. © 2025 IEEE.

关键词： Patient treatment

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computer Vision, imaging and computer graphics. Theory and Application: 7th international Joint conference, VISIGRAPP 2012, Rome, Italy, February 24-26, 2012, Revised Selected Papers

Computer Vision, Imaging and Computer Graphics. Theory and A...

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7th international Joint conference on computer Vision, imaging and computer graphics Theory and Applications, VISIGRAPP 2012

作者： Csurka, Gabriela Kraus, Martin Laramee, Robert S. Richard, Paul Braz, José Xerox Research Centre Europe 38240 Meylan France Aalborg University 9220 Aalborg Ø Denmark Swansea University Swansea SA2 8PP United Kingdom Université d'Angers LISA-ISTIA 49000 Angers France Escola Superior de Tecnologia do IPS 2910 Setúbal Portugal

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Leveraging ResNet50 for Accurate Brain Tumor Diagnosis

Leveraging ResNet50 for Accurate Brain Tumor Diagnosis

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Computational Intelligence, Communication Technology and Networking (CICTN), international conference on

作者： Gera Ranvitha Pureti Lakshmi Suhitha Ghantasala Lakshmi Prasanna Avuthu Lasya T. Bharath Kumar VIT-AP University Amaravati Andhra Pradesh School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Brain tumors are one of the foremost life-threatening diseases, producing serious complications that contribute to a heightened rate of morbidity and mortality. The emergence of deep learning strategies has vastly refined the process of identifying and categorizing brain tumors using MRI scans, providing crucial insights into the visualization of various structures within the human brain. Annually, countless individuals around the world receive a life-changing diagnosis of a brain tumor, and these cases often have low survival rates, underscoring the need for improved diagnostic approaches. This highlights the necessity for developing capable and systematic measures (improvement strategies) for diagnostics and treatment. While there has been progress in this regard, the search for a reliable detection system continues. In this paper, we focus on the ResNet50 model to enhance accuracy in detecting brain tumors. A type-curated ResNet50 is implemented for the brain tumor detection task, classifying MRI scans into two categories: 0 (Tumor) and 1 (No Tumor). Our findings reveal that the ResNet50 architecture successfully accomplished a classification accuracy of 97.3%, surpassing the VGG19 model, which attained an accuracy of 96%. This research seeks to contribute to the development of a robust and resilient model that enables healthcare professionals to make early and accurate decisions regarding brain tumors.

关键词： Deep learning Accuracy Magnetic resonance imaging Transfer learning Brain tumors Brain modeling Feature extraction Medical diagnostic imaging Tuning Residual neural networks

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Detection of Bone Cancer using Machine Learning: A Multi-Model Approach

Detection of Bone Cancer using Machine Learning: A Multi-Mod...

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Electronics and Renewable Systems (ICEARS), international conference on

作者： Pushya Saie Raag Enuga Sanjali Singh V Santhi School of Computer Science and Engg. Vellore Institute of Technology Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Bone cancer diagnosis remains a critical challenge due to the intricate nature of medical imaging data and the need for precise interpretation. This study introduces a machine learning framework leveraging Convolutional Neural Networks (CNNs) and transfer learning to automate bone cancer detection. We benchmarked four models—ResNet50, ResNet101, EfficientNet-B7, and a custom lightweight CNN—on a dataset comprising over 8,000 X-ray images. Among these, ResNet101 demonstrated the highest diagnostic performance, achieving an accuracy of 94.46 %, which surpasses ResNet50 by 4.26 %, EfficientNet-B7 by 32.46 %, and the custom CNN by 7.39 %. The custom CNN, designed for low-power scenarios, achieved a commendable accuracy of 87.07 %, highlighting its suitability for resource-constrained environments. Data augmentation techniques, including random flipping, rotation (±20°), zooming (0.8 to 1.2), and contrast adjustment (±20 %), enhanced model generalization. Grad-CAM visualizations offered interpretable insights into model predictions, empowering clinicians with clear decision-making frameworks. Comparative analysis with existing literature underscores significant advancements, identifies remaining gaps, and suggests opportunities for future research. This work highlights the promise of CNN-based solutions in advancing medical diagnostics and provides a roadmap for deploying efficient AI systems in clinical practice.

关键词： Accuracy Transfer learning Decision making Data visualization Bones Data models Convolutional neural networks X-ray imaging Cancer Residual neural networks

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