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An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

computer Modeling in Engineering & sciences 2023年第5期135卷 1571-1587页

作者： Jun Liu Geng Yuan Changdi Yang Houbing Song Liang Luo Robotics Institute School of Computer ScienceCarnegie Mellon UniversityPittsburghPA15217USA Department of Electrical&Computer Engineering College of EngineeringNortheastern UniversityBostonMA02115USA Security and Optimization for Networked Globe Laboratory(SONG Lab) Embry-Riddle Aeronautical UniversityDaytona BeachFL32114USA School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China

The interpretability of deep learning models has emerged as a compelling area in artificial intelligence *** safety criteria for medical imaging are highly stringent,and models are required for an ***,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and ***,the interpretability of CNNs has come into the *** medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning ***,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these *** this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac *** enhanced GPU training system implemented interpretable global average pooling for graphics using deep *** deep learning tasks were *** included data management,neural network architecture,and *** system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment *** results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning *** model is lightweight and more convenient to deploy on mobile devices than transfer learning models.

关键词： Interpretable graphics training visualization image segmentation left ventricle CNNs global average pooling

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Retrospective of Kazakh-Russian Sign Language Corpus Formation 11

Retrospective of Kazakh-Russian Sign Language Corpus Formati...

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11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources, sign-lang@LREC-COLING 2024

作者： Imashev, Alfarabi Kydyrbekova, Aigerim Mukushev, Medet Sandygulova, Anara Islam, Shynggys Israilov, Khassan Makazhanov, Aibek Yessenbayev, Zhandos Department of Robotics Engineering Nazarbayev University Kazakhstan Computer Science Lab National Laboratory Astana Nazarbayev University Kazakhstan Public Association "Kazakh Deaf Society" Astana Branch Kazakhstan

ISBN: (纸本)9782493814302

Sign language (SL) is a mode of communication that, in most cases, relies on visual perception exclusively and uses the visual-gestural modality. The advent of machine learning techniques has expanded the range of potential applications, not only in industry but also in addressing societal needs. Previous research has already demonstrated encouraging outcomes in developing sign language recognition systems that are both quite accurate and resilient. Nevertheless, the effectiveness and use of algorithms are impacted not only by their accessibility but also, at times to a greater extent, by the presence of substantial quantities of pertinent data. At the start of the local sign language corpus collection in 2015, there was a notable deficit of local Kazakh-Russian Sign Language data available for computer vision and machine-learning tasks. There were already corpora of another lexically close language, Russian Sign Language, but they were aimed at and tailored for lingustic research. We initiated the procedure by collecting data appropriate for machine-learning purposes. The subsets have been incorporated into the principal corpus and will be subject to future enhancements and refinements. This paper provides an overview of the collected components of the Kazakh-Russian Sign Language Corpus and the resulting outcomes derived from them. © 2024 ELRA Language Resources Association: CC BY-NC 4.0.

关键词： Machine learning

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SceneSense: Diffusion Models for 3D Occupancy Synthesis from Partial Observation

SceneSense: Diffusion Models for 3D Occupancy Synthesis from...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Alec Reed Brendan Crowe Doncey Albin Lorin Achey Bradley Hayes Christoffer Heckman Department of Computer Science Intelligent Robotics Laboratory University of Colorado Boulder

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

When exploring new areas, robotic systems generally exclusively plan and execute controls over geometry that has been directly measured. This planning paradigm can lead to unintuitive exploration or replanning latency when entering areas that were previous obstructed from view. To address this we present SceneSense, a real-time 3D diffusion model for synthesizing 3D occupancy information from partial observations that effectively predicts these occluded or out of view geometries for use in future planning and control frameworks. SceneSense uses a running occupancy map and a single RGB-D camera to generate predicted geometry around the platform at runtime, even when the geometry is occluded or out of view. Our architecture ensures that SceneSense never overwrites observed free or occupied space. By preserving the integrity of the observed map, SceneSense mitigates the risk of corrupting the observed space with generative predictions. While SceneSense is shown to operate well using a single RGB-D camera, the framework is flexible enough to extend to additional modalities. Unlike existing models that necessitate multiple views and offline scene synthesis, or are focused on filling gaps in observed data, our findings demonstrate that SceneSense is an effective approach to estimating unobserved local occupancy information at runtime. Local occupancy predictions from SceneSense are shown to better represent the ground truth occupancy distribution during the test exploration trajectories than the running occupancy map. The source code can be found on our website: https://***/scenesense/

关键词： Geometry Three-dimensional displays Runtime Source coding Diffusion models Cameras Real-time systems Planning Trajectory Intelligent robots

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FAST: Factorizable Attention for Speeding up Transformers

arXiv

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arXiv 2024年

作者： Gerami, Armin Hoover, Monte Dulepet, Pranav S. Duraiswami, Ramani Perceptual Interfaces and Reality Laboratory Department of Computer Science University of Maryland College Park United States

Motivated by the factorization inherent in the original fast multipole method and the improved fast Gauss transform we introduce a factorable form of attention that operates efficiently in high dimensions. This approach reduces the computational and memory complexity of the attention mechanism in transformers from O(N2) to O(N). In comparison to previous attempts, our work presents a linearly scaled attention mechanism that maintains the full representation of the attention matrix without compromising on sparsification and incorporates the all-to-all relationship between tokens. We explore the properties of our new attention metric and conduct tests in various standard settings. Results indicate that our attention mechanism has a robust performance and holds significant promise for diverse applications where self-attention is used. © 2024, CC BY-NC-ND.

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SceneSense: Diffusion Models for 3D Occupancy Synthesis from Partial Observation

arXiv

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arXiv 2024年

作者： Reed, Alec Crowe, Brendan Albin, Doncey Achey, Lorin Hayes, Bradley Heckman, Christoffer the Intelligent Robotics Laboratory Department of Computer Science the University of Colorado Boulder United States

When exploring new areas, robotic systems generally exclusively plan and execute controls over geometry that has been directly measured. When entering space that was previously obstructed from view such as turning corners in hallways or entering new rooms, robots often pause to plan over the newly observed space. To address this we present SceneScene, a real-time 3D diffusion model for synthesizing 3D occupancy information from partial observations that effectively predicts these occluded or out of view geometries for use in future planning and control frameworks. SceneSense uses a running occupancy map and a single RGB-D camera to generate predicted geometry around the platform at runtime, even when the geometry is occluded or out of view. Our architecture ensures that SceneSense never overwrites observed free or occupied space. By preserving the integrity of the observed map, SceneSense mitigates the risk of corrupting the observed space with generative predictions. While SceneSense is shown to operate well using a single RGB-D camera, the framework is flexible enough to extend to additional modalities. SceneSense operates as part of any system that generates a running occupancy map ‘out of the box’, removing conditioning from the framework. Alternatively, for maximum performance in new modalities, the perception backbone can be replaced and the model retrained for inference in new applications. Unlike existing models that necessitate multiple views and offline scene synthesis, or are focused on filling gaps in observed data, our findings demonstrate that SceneSense is an effective approach to estimating unobserved local occupancy information at runtime. Local occupancy predictions from SceneSense are shown to better represent the ground truth occupancy distribution during the test exploration trajectories than the running occupancy map. Finally, we analyze example predictions and show that SceneSense provides reasonable, accurate, and useful predictions. Copyrigh

关键词： Forecasting

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Preference detection of the humanoid robot face based on EEG and eye movement

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Neural Computing and Applications 2024年第19期36卷 11603-11621页

作者： Wang, Pengchao Mu, Wei Zhan, Gege Wang, Aiping Song, Zuoting Fang, Tao Zhang, Xueze Wang, Junkongshuai Niu, Lan Bin, Jianxiong Zhang, Lihua Jia, Jie Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI and Robotics Ministry of Education Shanghai Engineering Research Center of AI and Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai200040 China Yiwu Research Institute of Fudan University Zhejiang Yiwu322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

The face of a humanoid robot can affect the user experience, and the detection of face preference is particularly important. Preference detection belongs to a branch of emotion recognition that has received much attention from researchers. Most of the previous preference detection studies have been conducted based on a single modality. In this paper, we detect face preferences of humanoid robots based on electroencephalogram (EEG) signals and eye movement signals for single modality, canonical correlation analysis fusion modality, and bimodal deep autoencoder (BDAE) fusion modality, respectively. We validated the theory of frontal asymmetry by analyzing the preference patterns of EEG and found that participants had higher alpha wave energy for preference faces. In addition, hidden preferences extracted by EEG signals were better classified than preferences from participants' subjective feedback, and also, the classification performance of eye movement data was improved. Finally, experimental results showed that BDAE multimodal fusion using frontal alpha and beta power spectral densities and eye movement information as features performed best, with the highest average accuracy of 83.13% for the SVM and 71.09% for the KNN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electroencephalography

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Towards AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Ahmed, Imran Ahmad, Misbah Siddiqi, Muftooh Ur Rehman Chehri, Abdellah Jeon, Gwangill Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom University of West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Hartpury University Department of Animal and Agriculture Gloucester United Kingdom Aston University College of Engineering and Physical Sciences Birmingham United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

In the rapidly advancing field of intelligent transportation systems, integrating Artificial Intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model's effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model's capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision-making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step towards recognising the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology. © 2025 IEEE.

关键词： Artificial intelligence (AI) autonomous vehicles deep leaning edge computing Internet of Vehicles (IoV)

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Shape Manipulation of Bevel-Tip Needles for Prostate Biopsy Procedures: A Comparison of Two Resolved-Rate Controllers 46

Shape Manipulation of Bevel-Tip Needles for Prostate Biopsy ...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Yanzhou Al-Zogbi, Lidia Liu, Jiawei Shepard, Lauren Ghazi, Ahmed Tokuda, Junichi Leonard, Simon Krieger, Axel Iordachita, Iulian Johns Hopkins University Department of Mechanical Engineering The Laboratory for Computational Sensing and Robotics BaltimoreMD United States Johns Hopkins University Laboratory of Computational Sensing and Robotics BaltimoreMD United States Johns Hopkins University Brady Urological Institute BaltimoreMD United States Harvard Medical School Department of Radiology Brigham and Women's Hospital BostonMA United States Johns Hopkins University Laboratory for Computational Sensing and Robotics Department of Computer Science BaltimoreMD United States

ISBN: (纸本)9798350371499

Prostate cancer diagnosis continues to encounter challenges, often due to imprecise needle placement in standard biopsies. Several control strategies have been developed to compensate for needle tip prediction inaccuracies, however none were compared against each other, and it is unclear whether any of them can be safely and universally applied in clinical settings. This paper compares the performance of two resolved-rate controllers, derived from a mechanics-based and a data-driven approach, for bevel-tip needle control using needle shape manipulation through a template. We demonstrate for a simulated 12-core biopsy procedure under model parameter uncertainty that the mechanics-based controller can better reach desired targets when only the final goal configuration is presented even with uncertainty on model parameters estimation, and that providing a feasible needle path is crucial in ensuring safe surgical outcomes when either controller is used for needle shape manipulation. © 2024 IEEE.

关键词： Biopsy

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A deep convolutional neural network for diabetic retinopathy detection via mining local and long-range dependence

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 153-166页

作者： Xiaoling Luo Wei Wang Yong Xu Zhihui Lai Xiaopeng Jin Bob Zhang David Zhang Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of TechnologyShenzhenChina Peng Cheng Laboratory ShenzhenChina Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina College of Big Data and Internet Shenzhen Technology UniversityShenzhenChina The Department of Computer and Information Science University of MacaoMacaoMacaoChina The Chinese University of Hong Kong(Shenzhen) ShenzhenChina

Diabetic retinopathy(DR),the main cause of irreversible blindness,is one of the most common complications of *** present,deep convolutional neural networks have achieved promising performance in automatic DR detection *** convolution operation of methods is a local cross-correlation operation,whose receptive field de-termines the size of the local neighbourhood for ***,for retinal fundus photographs,there is not only the local information but also long-distance dependence between the lesion features(*** and exudates)scattered throughout the whole *** proposed method incorporates correlations between long-range patches into the deep learning framework to improve DR ***-wise re-lationships are used to enhance the local patch features since lesions of DR usually appear as *** Long-Range unit in the proposed network with a residual structure can be flexibly embedded into other trained *** experimental results demon-strate that the proposed approach can achieve higher accuracy than existing state-of-the-art models on Messidor and EyePACS datasets.

关键词： image classification medical image processing pattern recognition

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Attention-Based Hybrid Deep Learning Model for Intrusion Detection in IIoT Networks 28th

Attention-Based Hybrid Deep Learning Model for Intrusion Det...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Ullah, Safi Boulila, Wadii Koubaa, Anis Ahmad, Jawad Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia Department of Computer Science Quaid-i-Azam University Islamabad44000 Pakistan RIADI Laboratory National School of Computer Science University of Manouba Manouba Tunisia School of Computing Edinburgh Napier University EdinburghEH10 5DT United Kingdom

The integration of Industrial Internet of Things (IIoT) technology into the industrial sector has produced numerous significant advantages. However, the notable concern remains the absence of robust security and privacy measures in these interconnected critical environments. To secure IIoT networks, several researchers and experts employ intrusion detection systems (IDS) for detecting cyberattacks. The current systems exhibit efficient performance when handling a few categories of attack classes, even in the presence of slight imbalances. However, these models face challenges when confronted with vast categories of attack classes and highly imbalanced data. To tackle these issues, this study introduces an attention-based hybrid deep learning (AB-HDL) model designed to monitor network traffic and predict cyberattacks within the network. The proposed model comprises an attention mechanism and a hybrid deep learning model that integrates convolutional neural networks (CNN) and an autoencoder (AE). The effectiveness of the proposed AB-HDL is assessed using publicly accessible datasets: Edge-IIoTset and X-IIoTID. To ascertain the efficacy of AB-HDL, a comparative analysis is conducted with various other machine learning (ML) and deep learning (DL) algorithms. The outcome analysis indicates that the proposed AB-HDL surpasses the performance of the other algorithms and exhibits optimal efficiency in detecting cyber attacks within IIoT networks. © 2024 The Authors.

关键词： Network intrusion

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