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A deep-learning Empowered, real-time processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2025年 33卷 1220-1230页

作者： Xia, Yunjia Chen, Jianan Li, Jinchen Gong, Tingchen Vidal-Rosas, Ernesto E. Loureiro, Rui Cooper, Robert J. Zhao, Hubin Univ Coll London UCL Div Surg & Intervent Sci HUB Intelligent Neuroengn HUBIN CREATe London WC1E 6BT England Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England UCL Dept Med Phys & Biomed Engn DOT HUB London WC1E 6BT England

Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are promising techniques for these applications due to their non-invasiveness, portability, low cost, and relatively high spatial resolution. However, real-time processing of fNIRS/DOT data remains a significant challenge as it requires establishing a baseline of the measurement, simultaneously performing real-time motion artifact (MA) correction across all channels, and (in the case of DOT) addressing the time-consuming process of image reconstruction. This study proposes a real-time processing system for fNIRS/DOT that integrates baseline calibration, denoising autoencoder (DAE) based MA correction model with a sliding window strategy, and a pre-calculated inverse Jacobian matrix to streamline the reconstructed 3D brain hemodynamics. The DAE model was trained on an extensive whole-head high-density DOT (HD-DOT) dataset and tested on separate motor imagery dataset augmented with artificial MA. The system demonstrated the capability to simultaneously process approximately 750 channels in real-time. Our results show that the DAE-based MA correction method outperformed traditional MA correction in terms of mean squared error and correlation to the known MA-free data while maintaining low latency, which is critical for effective BCI and NFB applications. The system's high-channel, real-time processing capability provides channel-wise oxygenation information and functional 3D imaging, making it well-suited for fNIRS/DOT applications in BCI and NFB, particularly in movement-intensive scenarios such as motor rehabilitation and assistive technology for mobility support.

关键词： real-time systems US Department of Transportation Hemodynamics Functional near-infrared spectroscopy Optical imaging Three-dimensional displays Feedback amplifiers Jacobian matrices image reconstruction Electroencephalography Functional near-infrared spectroscopy (fNIRS) diffuse optical tomography (DOT) brain-computer interface (BCI) neurofeedback (NFB) motion artifacts deep learning real-time processing

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deep learning and Transfer learning in Cardiology: A Review of Cardiovascular Disease Prediction Models

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IEEE ACCESS 2024年 12卷 193365-193386页

作者： Sunilkumar, G. Kumaresan, P. Vellore Inst Technol Sch Comp Sci Engn & Informat Syst SCORE Vellore 632014 India

Cardiovascular disorders are the primary cause of death on a global scale. The World Health Organization report indicates that approximately 18 million people die from CVD each year. Major cardiac risks include arrhythmia and coronary artery disease, among others. Recent advancements in Artificial Intelligence play a pivotal role for life-saving interventions in CVD treatment. This survey examines the latest progress in Machine learning, deep learning and Pre-trained transfer learning models for classifying and predicting CVD using a survey of 159 articles, including 36 image datasets, 41 signal data and 52 clinical data from various sources. The survey investigates cardiac risk factors, cardiac dysfunction classification, various modalities and medical image processing techniques, performance metrics and hybrid techniques. Surveys on traditional neural networks such as Convolutional Neural Networks, Artificial Neural Networks, and Recurrent Neural Networks often achieve an accuracy rate of 70% to 95%. Leveraging pre-trained architectures such as ResNet, DenseNet, AlexNet, MobileNet, EfficientNet and GoogLeNet, transfer learning models consistently outperform other approaches frequently achieving accuracy levels greater than 96%. Researchers utilize various hybrid optimization algorithms to improve overall accuracy rate. The outcome of the survey supports a precise prognosis for patients with comorbidities. The survey findings indicate limitations in the incorporation of multimodal data for real-time risk assessment. The study results possess the capacity to bridge gaps in research on cardiovascular disease prediction, thereby assisting medical practitioners in early identification and subsequent prognosis.

关键词： Diseases Arteries Cardiovascular diseases Solid modeling Biomedical imaging Surveys deep learning Transfer learning Blood flow Blood Cardiovascular disease machine learning deep CNN transfer learning optimization hybrid model

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StrainNet-LD: Large Displacement digital image correlation based on deep learning and displacement-field decomposition

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OPTICS AND LASERS IN ENGINEERING 2024年 183卷

作者： Wang, Guowen Zhou, Yuan Wang, Zhiyuan Zhou, Jian Xuan, Shanyong Yao, Xuefeng Tsinghua Univ Dept Engn Mech Appl Mech Lab Beijing 100084 Peoples R China Wuhu Machinery Factory 99 Nanyang Rd Wuhu 24100 Peoples R China

First, a displacement-field decomposition strategy is proposed that effectively enhances the accuracy of digital image correlation (DIC) algorithms under large displacement or deformation conditions. The implementation methodologies of displacement-field decomposition under both Lagrangian and Eulerian descriptions are derived and presented. Second, displacement-field decomposition is applied to speckle image correlation algorithms based on deep learning, resolving the contradiction between registration capability of large displacement and the accuracy. A CUDA-accelerated and residual learning-based strain fitting algorithm is proposed for high-precision and real-time strain computation. This method is named "StrainNet for Large Displacement" (StrainNet-LD). StrainNet-LD achieves a calculation speed of 768 x 768 x 6 fps (768 x 768 x 3 fps with strain calculated simultaneously) on GPU with displacement MAE < 0.05 pixels and strain MAE < 0.002 epsilon. Finally, rubber tensile tests, 3D motion measurement of composite shells, and morphology reconstruction experiments of speckle-free composite repair structures are conducted to validate the algorithm's robustness and efficiency. Some vital discussions of displacement-field decomposition are given in the Appendixes. The code and dataset are available at https://***/GW-Wang-thu/StrainNet-LD.

关键词： Digital image correlation (DIC) Displacement field decomposition deep learning Large deformation real time Composite repair structure

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ETransCap: efficient transformer for image captioning

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APPLIED INTELLIGENCE 2024年第21期54卷 10748-10762页

作者： Mundu, Albert Singh, Satish Kumar Dubey, Shiv Ram IIIT Allahabad Dept IT Comp Vis & Biometr Lab CVBL Allahabad India

image captioning is a challenging task in computer vision that automatically generates a textual description of an image by integrating visual and linguistic information, as the generated captions must accurately describe the image's content while also adhering to the conventions of natural language. We adopt the encoder-decoder framework employed by various CNN-RNN-based models for image captioning in the past few years. Recently, we observed that the CNN-Transformer-based models have achieved great success and surpassed traditional CNN-RNN-based models in the area. Many researchers have concentrated on Transformers, exploring and uncovering its vast possibilities. Unlike conventional CNN-RNN-based models in image captioning, transformer-based models have achieved notable success and offer the benefit of handling longer input sequences more efficiently. However, they are resource-intensive to train and deploy, particularly for large-scale tasks or for tasks that require real-time processing. In this work, we introduce a lightweight and efficient transformer-based model called the Efficient Transformer Captioner (ETransCap), which consumes fewer computation resources to generate captions. Our model operates in linear complexity and has been trained and tested on MS-COCO dataset. Comparisons with existing state-of-the-art models show that ETransCap achieves promising results. Our results support the potential of ETransCap as a good approach for image captioning tasks in real-time applications. Code for this project will be available at https://***/albertmundu/etranscap.

关键词： deep learning Natural language processing image captioning Scene understanding Transformers Efficient transformers

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real-time binocular visual localization system based on the improved BGNet stereo matching framework

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JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS image SCIENCE AND VISION 2024年第3期41卷 500-509页

作者： Qu, Zanxi Li, Li Jin, Weiqi Yang, Ye Beijing Inst Technol Key Lab Photoelect Imaging Technol & Syst Beijing 100081 Peoples R China Key Lab Low Light Night Vis Technol Xian 710065 Shanxi Peoples R China

Binocular vision technology is widely used to acquire three-dimensional information of images because of its low cost. In recent years, the use of deep learning for stereo matching has shown promising results in improving the measurement stability of binocular vision systems, but the real-time performance in high -precision networks is typically poor. Therefore, this study constructed a deep -learning -based stereo matching binocular vision system based on the BGLGA-Net, which combines the advantages of past networks. Experiments showed that the ability to detect the edges of foreground objects was enhanced. The network was used to build a system on the Xavier NX. The measurement accuracy and stability were better than those of traditional algorithms. (c) 2024 Optica Publishing Group

关键词： deep learning Imaging systems Machine vision Physiology Stereo matching Three dimensional imaging

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YOLO9tr: a lightweight model for pavement damage detection utilizing a generalized efficient layer aggregation network and attention mechanism

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JOURNAL OF real-time image processing 2024年第5期21卷 163页

作者： Youwai, Sompote Chaiyaphat, Achitaphon Chaipetch, Pawarotorn King Mongkuts Univ Technol Thonburi Dept Civil Engn AI Res Grp Bangkok Thailand Infraplus Co Ltd Bangkok Thailand

Maintaining road pavement integrity is crucial for ensuring safe and efficient transportation. Conventional methods for assessing pavement condition are often laborious and susceptible to human error. This paper proposes YOLO9tr, a novel lightweight object detection model for pavement damage detection, leveraging the advancements of deep learning. YOLO9tr is based on the YOLOv9 architecture, incorporating a partial attention block that enhances feature extraction and attention mechanisms, leading to improved detection performance in complex scenarios. The model is trained on a comprehensive dataset comprising road damage images from multiple countries. This dataset includes an expanded set of damage categories beyond the standard four types (longitudinal cracks, transverse cracks, alligator cracks, and potholes), providing a more nuanced classification of road damage. This broadened classification range allows for a more accurate and realistic assessment of pavement conditions. Comparative analysis demonstrates YOLO9tr's superior precision and inference speed compared to state-of-the-art models like YOLOv8, YOLOv9 and YOLOv10, achieving a balance between computational efficiency and detection accuracy. The model achieves a high frame rate of up to 136 FPS, making it suitable for real-time applications such as video surveillance and automated inspection systems. The research presents an ablation study to analyze the impact of architectural modifications and hyperparameter variations on model performance, further validating the effectiveness of the partial attention block. The results highlight YOLO9tr's potential for practical deployment in real-time pavement condition monitoring, contributing to the development of robust and efficient solutions for maintaining safe and functional road infrastructure.

关键词： YOLO Pavement damage Object detection deep learning

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2-D/3-D Medical image Registration Based on Feature-Point Matching

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷

作者： Si, Shengyuan Li, Zheng Lin, Ze Xu, Xian Zhang, Yudong Xie, Shipeng Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing 210003 Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Jiangsu Peoples R China

Two-dimensional/3-D medical image registration has a wide range of applications in intraoperative image-guided navigation, which can not only assist surgeons in accurately locating lesions but also serve as a key link for surgical robots to locate the surgical site. Current methods for 2-D/3-D spine image registration are susceptible to getting stuck in local optimization, struggling to extract gradient information from noisy real data, and exhibiting slow processing speeds. Recently, deep learning methods have suffered from insufficient training data, poor generalization performance, and a tendency to produce incorrect solutions. We propose an optimized model that significantly improves the speed and accuracy of 2-D/3-D registration by deeply integrating a feature-point matching network. This network demonstrates exceptional robustness in processing high-noise imagery and is adept at coarse registration, providing the initial solution for the optimized model and thereby abbreviating the time required for coarse registration. It also facilitates updates of parameter location modules within the optimized model, diminishing the overall computational demand. Additionally, by harnessing grayscale and spinal feature information, we formulate an objective function enriched with a feature-point similarity metric to govern the descent trajectory, culminating in heightened precision and expedited convergence. Our empirical findings indicate that this method achieves a mean accuracy of 0.2550 mm on real data, substantiating the efficacy of our approach.

关键词： Feature extraction Accuracy X-rays Linear programming Three-dimensional displays Telecommunications Surgery image registration deep learning Data models 2-D/3-D registration digital reconstruction radiography (DRR) feature-point matching network similarity measurement spine image registration

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real-time Moving Object Detection for 3-D LiDAR Using Occlusion Accumulation in Range image

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Kim, Junha Kim, Haram Oh, Changsuk Kim, Changhyeon Kim, H. Jin Seoul Natl Univ Dept Mech & Aerosp Engn Seoul 08826 South Korea Seoul Natl Univ Automat & Syst Res Inst ASRI Seoul 08826 South Korea 42dot Seoul 13449 South Korea Seoul Natl Univ Artificial Intelligence Inst AIIS Seoul 08826 South Korea Samsung Res Seoul 06765 South Korea Seoul Natl Univ Dept Aerosp Engn Seoul 08826 South Korea

deep learning methods have been applied to detect moving objects from 3-D LiDAR data, but they require extra computing devices, such as graphics processing units (GPUs), and often struggle to operate at real-time LiDAR frame rates. In addition, retraining is necessary for new environments, which demands significant resources. Nonlearning methods that operate online without prior maps and rely solely on a central processing unit (CPU) often fail to achieve real-time performance. Furthermore, they are limited in applicability due to their dependence on specific pose estimation algorithms. To address these issues, we propose a novel occlusion accumulation framework in the range image domain for real-time 3-D LiDAR moving object detection on a CPU. By incorporating compensation strategies that account for LiDAR artifacts and measurement sparsity, the proposed method reduces false positives (FPs) and improves detection accuracy. Our approach is also flexible, integrating seamlessly with different pose estimation algorithms without performance degradation. Extensive experiments on KITTI, Apollo, and CARLA datasets demonstrate that the proposed method achieves competitive results compared to state-of-the-art (SOTA) learning-based methods on KITTI and outperforms them in cross-validation on Apollo and CARLA. Moreover, the proposed method operates four times faster than SOTA nonlearning-based methods while offering higher detection accuracy, making it highly suitable for real-world applications in autonomous driving and robotic navigation. We provide all the source code and datasets used in this article to the public at: https://***/JunhaAgu/Mapless_Moving.

关键词： 3-D LiDAR moving object detection occlusion accumulation occlusion accumulation range image range image range image

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QARV: Quantization-Aware ResNet VAE for Lossy image Compression

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第1期46卷 436-450页

作者： Duan, Zhihao Lu, Ming Ma, Jack Huang, Yuning Ma, Zhan Zhu, Fengqing Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA Nanjing Univ Sch Elect Sci & Engn Nanjing 210093 Jiangsu Peoples R China

This paper addresses the problem of lossy image compression, a fundamental problem in image processing and information theory that is involved in many real-world applications. We start by reviewing the framework of variational autoencoders (VAEs), a powerful class of generative probabilistic models that has a deep connection to lossy compression. Based on VAEs, we develop a new scheme for lossy image compression, which we name quantization-aware ResNet VAE (QARV). Our method incorporates a hierarchical VAE architecture integrated with test-time quantization and quantization-aware training, without which efficient entropy coding would not be possible. In addition, we design the neural network architecture of QARV specifically for fast decoding and propose an adaptive normalization operation for variable-rate compression. Extensive experiments are conducted, and results show that QARV achieves variable-rate compression, high-speed decoding, and better rate-distortion performance than existing baseline methods.

关键词： Lossy image compression learned image compression variational autoencoder deep learning

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An Evaluation Method of Dental Treatment Quality Combined with deep learning and Multi-index Decomposition

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APPLIED ARTIFICIAL INTELLIGENCE 2024年第1期38卷

作者： Peng, Gang Liu, Jie Yan, Feng Liu, Beicun Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat 1037 Luoyu Rd Wuhan Hubei Peoples R China Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Hubei Eya Med Investment Management Co Ltd Res Ctr Wuhan Peoples R China

Dentists judge that the quality of dental treatment for each patient is very time-consuming and inefficient, lacks quantitative evaluation criteria, and is easy to cause errors. At the same time, the traditional method of extracting tooth and root canal image features based on experience is difficult to accurately extract the tooth area and root canal filling area, resulting in low accuracy of tooth and root canal segmentation, which in turn affects the accuracy of tooth treatment quality evaluation. In this paper, a deep learning convolutional neural network is used to segment the root canal filling area, tooth boundary, and the boundary between tooth and soft tissue for the real patient 's root canal treatment and filling image. Finally, the segmented image is quantitatively evaluated according to the multi-evaluation index of professional doctors. The experimental results show that the intelligent evaluation method of dental treatment quality combined with deep learning and multi-index decomposition proposed in this paper not only unifies the evaluation criteria of dental treatment quality but also the therapeutic effect of quantitative scoring can effectively improve the work efficiency of doctors, which has reference significance for the application of artificial intelligence in the medical field.

关键词： deep learning

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