检索结果-内蒙古大学图书馆

CT synthesis with deep learning for MR-only radiotherap planning: a review

BIOMEDICAL ENGINEERING LETTERS 2024年第6期14卷 1259-1278页

作者： Roh, Junghyun Ryu, Dongmin Lee, Jimin Ulsan Natl Inst Sci & Technol Grad Sch Artificial Intelligence 50 Unist Gil Ulsan 44919 South Korea Seoul Natl Univ Program Biomed Radiat Sci 71 Ihwajang Gil Seoul 03087 South Korea Ulsan Natl Inst Sci & Technol Dept Nucl Engn 50 Unist Gil Ulsan 44919 South Korea Ulsan Natl Inst Sci & Technol Grad Sch Hlth Sci & Technol 50 Unist Gil Ulsan 44919 South Korea

MR-only radiotherapy planning is beneficial from the perspective of both time and safety since it uses synthetic CT for radiotherapy dose calculation instead of real CT scans. To elevate the accuracy of treatment planning and apply the results in practice, various methods have been adopted, among which deep learning models for image-to-image translation have shown good performance by retaining domain-invariant structures while changing domain-specific details. In this paper, we present an overview of diverse deep learning approaches to MR-to-CT synthesis, divided into four classes: convolutional neural networks, generative adversarial networks, transformer models, and diffusion models. By comparing each model and analyzing the general approaches applied to this task, the potential of these models and ways to improve the current methods can be can be evaluated.

关键词： MR-to-CT synthesis deep learning Computer vision MR-only radiotherapy planning

来源：评论

学校读者我要写书评

暂无评论

TLEAR-Net: A Network for Defect Detection in Train Wheelset Treads Based on Transfer learning and Edge Adaptive Reinforcement Attention

引用

IET image processing 2025年第1期19卷

作者： Hu, Xinliang He, Jing Zhang, Changfan Cheng, Xiang Chenzhou Vocat Tech Coll Sch Modern Equipment Mfg Chenzhou Hunan Peoples R China Hunan Univ Technol Sch Elect & Informat Engn Zhuzhou Hunan Peoples R China Hunan Railway Profess Technol Coll Dept Intelligent Control Zhuzhou Hunan Peoples R China

As a critical load-bearing and running component of railway systems, the wheelset's operational safety fundamentally depends on precise detection and localisation of tread defects. Current deep learning-based detection methods face significant challenges in extracting discriminative edge features under small-sample conditions, leading to suboptimal defect localisation accuracy. To address these limitations, this study proposes TLEAR-Net, a novel defect detection framework integrating transfer learning with an edge-adaptive reinforcement attention mechanism. The methodology employs RetinaNet as the baseline architecture, enhanced through multi-stage domain adaptation using COCO 2017 pretraining and parameter-shared ResNet-50 backbone optimisation to bridge cross-domain feature discrepancies. An innovative edge-adaptive reinforcement (EAR) attention module is developed to selectively amplify defect boundary features through learnable gradient operators and hybrid spatial-channel attention mechanisms. Comprehensive evaluations on a proprietary data set annotated defect samples demonstrate the framework's superior performance, achieving state-of-the-art detection accuracy (89.22% mAP) while maintaining real-time processing capability (42.45 FPS).

关键词： big data CCD image sensors convolutional neural nets crack detection decision making edge detection learning (artificial intelligence) neural net architecture non-destructive testing object detection

来源：评论

学校读者我要写书评

暂无评论

A dual attentional skip connection based Swin-UNet for real-time cloud segmentation

引用

IET image processing 2024年第12期18卷 3460-3479页

作者： Wei, Fuhao Wang, Shaofan Sun, Yanfeng Yin, Baocai Beijing Univ Technol Sch Informat Sci & Technol Beijing Key Lab Multimedia & Intelligent Software Beijing Peoples R China

Developing real-time cloud segmentation technology is urgent for many remote sensing based applications such as weather forecasting. Existing deep learning based cloud segmentation methods involve two shortcomings. (a): They tend to produce discontinuous boundaries and fail to capture less salient feature, which corresponds to thin cloud pixels;(b): they are unrobust towards different scenarios. Those issues are circumvented by integrating U-Net and the swin transformer together, with an efficiently designed dual attention mechanism based skip connection. Typically, a swin transformer based encoder-decoder network, by incorporating a dual attentional skip connection with Swin-UNet (DASUNet) is proposed. DASUNet captures the global relationship of image patches based on its window attention mechanism, which fits the real-time requirement. Moreover, DASUNet characterizes the less salient features by equipping with token dual attention modules among the skip connection, which compensates the ignorance of less salient features incurred from traditional attention mechanism during the stacking of transformer layers. Experiments on ground-based images (SWINySeg) and remote sensing images (HRC-WHU, 38-Cloud) show that, DASUNet achieves the state-of-the-art or competitive results for cloud segmentation (six top-1 positions of six metrics among 11 methods on SWINySeg, two top-1 positions of five metrics among 10 methods on HRC-WHU, two top-1 positions of four metrics among 12 methods with ParaNum <= 30M$\le 30{\rm M}$ on 38-Cloud), with 100FPS implementation speed averagely for each 224x224$224\times 224$ image. We propose a swin transformer based encoder-decoder network, by incorporating a dual attentional skip connection with Swin-UNet (DASUNet) for cloud segmentation. DASUNet characterizes the less salient features by equipping with token dual attention modules among the skip connection. The token dual attention module consists of token similarity attention and token impor

关键词： feature selection image processing image segmentation

来源：评论

学校读者我要写书评

暂无评论

HIDS-IoMT: A deep learning-Based Intelligent Intrusion Detection System for the Internet of Medical Things

引用

IEEE ACCESS 2025年 13卷 32863-32882页

作者： Berguiga, Abdelwahed Harchay, Ahlem Massaoudi, Ayman Jouf Univ Coll Comp & Informat Sci Dept Comp Sci Sakaka 72388 Saudi Arabia Univ Carthage Mediatron Res Lab Higher Sch Commun Ariana 1054 Tunisia

The expansion of the Internet of Medical Things (IoMT) has enhanced the accuracy, real-time functionality, connectivity, and intelligence of medical examination practices. However, increased interconnectivity of medical equipment has rendered IoMT networks susceptible to several cyberattacks, particularly the Distributed Denial of Service (DDoS) attacks. Current intrusion detection systems (IDS) are insufficiently focused to address and counteract these advanced threats. Furthermore, due to the dynamic nature of IoMT traffic, IDS has considerable difficulty preserving its current threat detection capabilities. This study presents a hybrid deep learning-based intrusion detection system for IoMT networks (HIDS-IoMT). The proposed model hybridizes the Convolutional Neural Network (CNN) for feature extraction and the Long Short Term Memory neural network (LSTM) for sequence data prediction. We implement the designed IDS on a Raspberry Pi device using a fog computing architecture, enabling decentralized processing closer to IoMT devices, thereby enhancing responsiveness and reducing latency. We evaluate the suggested approach for intrusion detection using the IoTID20 and the Edge-IIoTset datasets. These datasets comprise a substantial and varied assortment of traffic flows from actual DDoS attacks, including SYN floods, UDP floods, HTTP floods, and others. We evaluated our novel methodology against distinct DoS attacks such as DDoS, SQL injection, Vulnerability scanner, Scan Host Port, Mirai, etc. Our proposed model attains an accuracy of 99.92%, a precision of 99.91%, a recall rate of 99.99%, and an F1-score of 99.95%, outperforming the latest in advanced methodologies.

关键词： Accuracy image edge detection Convolutional neural networks Intrusion detection Denial-of-service attack Computer architecture Training Telecommunication traffic real-time systems Neural networks Internet of Medical Things DDoS attacks intrusion detection system deep learning convolutional neural network long short term memory neural network

来源：评论

学校读者我要写书评

暂无评论

Adaptive Feature Aggregation Centric Enhance Network for Accurate and Fast Monocular 3-D Object Detection

引用

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Lin, Peng-Wei Hsu, Chih-Ming Natl Taipei Univ Technol Inst Mech & Elect Engn Taipei 10608 Taiwan Natl Taipei Univ Technol Dept Mech Engn Taipei 10608 Taiwan

Three-dimensional object detection is crucial in autonomous driving. Monocular 3-D object detection has become a popular area of research in autonomous driving because of its ease of deployment and cost-effectiveness. In real-world autonomous driving, detectors should be both real time and accurate. These features can be achieved using deep learning (DL). A one-stage center-based object detector is suitable for real-world applications. However, in center-based object detectors, object-centric estimation plays an important role because it significantly influences detection results. To address this issue, we propose a real-time monocular 3-D object detection neural network called the adaptive feature aggregate centric enhance network. The proposed model is an anchor-free and center-based method. To improve accuracy while maintaining inference speed, we propose an adaptive feature aggregation network that aggregates multiscale features by weighting. Furthermore, we propose a centric enhanced module for heatmap prediction to improve the accuracy of object localization and classification. Our model can achieve 34.48 fps using an Nvidia RTX3070 graphy processing unit (GPU). Extensive experiments on the KITTI benchmark demonstrate that our method achieves good average precision (AP) for cars and pedestrians.

关键词： Three-dimensional displays Heating systems Feature extraction Accuracy Detectors Head Location awareness Convolution Autonomous vehicles Aggregates 3-D object detection attention mechanism autonomous driving deep learning (DL) monocular image real time

来源：评论

学校读者我要写书评

暂无评论

A deep learning based architecture for multi-class skin cancer classification

引用

Multimedia Tools and Applications 2024年第39期83卷 87105-87127页

作者： Mushtaq, Snowber Singh, Omkar Srinagar190006 India

One of the deadliest forms of skin cancer is malignant melanoma, developed by aberrant melanocyte cell development. Efficient diagnostic procedures are essential due to the rising prevalence of skin illnesses resulting from several variables such as infections, allergies, dietary habits, chemical exposure, genetic sensitivities, and lifestyle changes. An increasing number of deep Convolutional Neural Network (DCNN) prototypes are used for the computerized identification of skin cancer using dermoscopic images. The research aims to develop a detailed classification framework for the multi-class categorization of skin cancer carcinoma at an earlier phase using Hair removal image processing Technique and Ensemble Visual Geometry Group-16 (EVGG-16) based on a Convolution Neural Network. The main idea is to set different initializations to the same VGG-16 model three times and then to Ensemble the results. The Experimental results show that the VGG-16 model receives an average accuracy of 87% and an F1 score of 86%. The proposed Ensemble Visual Geometry Group-16 (EVGG-16) model receives an average accuracy of 88% and an F1-score of 88% without the hair removal image processing technique;the same model receives an average accuracy of 89% and an F1-score of 88% after applying the hair removal image processing technique. The publicly accessible HAM1000 dataset was employed to experiment;it includes processed images belonging to seven classes to evaluate the model's effectiveness. In conclusion, our proposed Ensemble model with Hair removal technique performs better than other cutting-edge deep-learning techniques and skilled dermatologists. Furthermore, this might be a trustworthy real-time teaching resource for medical students in a dermatology program. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： learning systems

来源：评论

学校读者我要写书评

暂无评论

TinyCount: an efficient crowd counting network for intelligent surveillance

引用

JOURNAL OF real-time image processing 2024年第4期21卷 153页

作者： Lee, Hyeonbeen Lee, Jangho Incheon Natl Univ Dept Comp Sci & Engn Incheon 22012 South Korea

Crowd counting, the task of estimating the total number of people in an image, is essential for intelligent surveillance. Integrating a well-trained crowd counting network into edge devices, such as intelligent CCTV systems, enables its application across various domains, including the prevention of crowd collapses and urban planning. For a model to be embedded in edge devices, it requires robust performance, reduced parameter count, and faster response times. This study proposes a lightweight and powerful model called TinyCount, which has only 60k parameters. The proposed TinyCount is a fully convolutional network consisting of a feature extract module (FEM) for robust and rapid feature extraction, a scale perception module (SPM) for scale variation perception and an upsampling module (UM) that adjusts the feature map to the same size as the original image. TinyCount demonstrated competitive performance across three representative crowd counting datasets, despite utilizing approximately 3.33 to 271 times fewer parameters than other crowd counting approaches. The proposed model achieved relatively fast inference times by leveraging the MobileNetV2 architecture with dilated and transposed convolutions. The application of SEblock and findings from existing studies further proved its effectiveness. Finally, we evaluated the proposed TinyCount on multiple edge devices, including the Raspberry Pi 4, NVIDIA Jetson Nano, and NVIDIA Jetson AGX Xavier, to demonstrate its potential for practical applications.

关键词： Crowd counting Lightweight network Intelligent surveillance Artificial intelligence deep learning Edge machine learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing passive gamma emission tomography data with deep learning

引用

ANNALS OF NUCLEAR ENERGY 2024年 204卷

作者： Sanchez-Belenguer, Carlos Casado-Coscolla, Alvaro Wolfart, Erik Joint Res Ctr European Commiss Via Enrico Fermi 2749 I-21027 Ispra VA Italy Seidor Italy SRL Contract European Commiss Ispra Italy

In this paper, we address the problem of generating and enhancing Passive Gamma Emission Tomography (PGET) data from a deep learning perspective. The PGET instrument has been developed for the verification of spent nuclear fuel and relies on image reconstruction and analysis algorithms to detect missing or substituted fuel pins. Such techniques are sensitive to the quality of the input data: noisy or incomplete sinograms yield to poor reconstructions and, consequently, to low-confidence results. The development and validation of these algorithms is based on complex Monte Carlo simulations that are time-consuming and computationally-demanding. We propose the use of Convolutional Neural Networks (CNNs) for enhancing PGET data. Our technique learns the mapping between incomplete or noisy sinograms and their corresponding full representation. It effectively exploits the high degree of redundancy of the measurements, i.e . the contribution of a single pin can be observed from many different directions, to learn the underlying model of the data and to make informed predictions. The two main applications of our approach are: (1) accelerating Monte Carlo simulations and (2) pre-processing real measurements to enhance them before running the standard image reconstruction and analysis techniques. The experimental evaluation was performed with both, simulated and real measurements. Results show how effectively CNNs can learn and exploit the structure of the data. For the two use cases evaluated, denoising sinograms and inpainting incomplete ones, our technique achieved state-of-the-art performance with execution times in the order of milliseconds.

关键词： Passive gamma emission tomography deep learning Monte Carlo simulations

来源：评论

学校读者我要写书评

暂无评论

A Video Frame Extrapolation Scheme Using deep learning-Based Uni-Directional Flow Estimation and Pixel Warping

引用

IEEE ACCESS 2023年 11卷 105885-105891页

作者： Ban, Tae-Won Gyeongsang Natl Univ Dept Intelligent Commun Engn Jinju 52828 Gyeongsangnam South Korea

This paper investigates video frame extrapolation, which can predict future frames from current and past frames. Although there have been many studies on video frame extrapolation in recent years, most of them suffer from the unsatisfactory image quality of the predicted frames such as severe blurring because it is difficult to predict the movement of future pixels for multi-modal video frames, especially with fast changing frames. An additional process such as frame alignment or recurrent prediction can improve the quality of the predicted frames, but it hinders real-time extrapolation. Motivated by the significant progress in video frame interpolation using deep learning-based flow estimation, a simplified video frame extrapolation scheme using deep learning-based uni-directional flow estimation is proposed to reduce the processing time compared to conventional video frame extrapolation schemes without compromising the image quality of the predicted frames. In the proposed scheme, the uni-directional flow is first estimated from the current and past frames through a flow network consisting of four flow blocks and the current frame is forward-warped through the estimated flow to predict a future frame. The proposed flow network is trained and evaluated using the Vimeo-90K triplet dataset. The performance of the proposed scheme is analyzed using the trained flow network in terms of prediction time as well as the similarity between predicted and ground truth frames such as the structural similarity index measure and mean absolute error of pixels, and compared to that of the state-of-the-art schemes such as Iterative and cycleGAN schemes. Extensive experiments show that the proposed scheme improves prediction quality by 2.1% and reduces prediction time by 99.7% compared to the state-of-the-art scheme.

关键词： Video frame extrapolation video frame prediction flow estimation flow network deep learning

来源：评论

学校读者我要写书评

暂无评论

Trends and Applications of On-Board image processing for Earth Observation Nanosatellites: A Systematic Review

引用

INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES 2025年 1-31页

作者： Chanoui, Mohammed Alae Khalil, Imane Sbihi, Mohammed Ismaili, Zine El Abidine Alaoui Guennoun, Zouhair Mohammed V Univ Rabat Higher Sch Technol Sale Lab Syst Anal Informat Proc & Ind Management LASTI Rabat Morocco Mohammed V Univ Rabat Univ Ctr Res Space Technol CURTS Mohammadia Sch Engineers Rabat Morocco Mohammed V Univ Rabat Mohammadia Sch Engineers Smart Commun Res Team ERSC Rabat Morocco Mohammed V Univ Rabat Natl Sch Comp Sci & Syst Anal Informat Commun & Embedded Syst Team ICES Rabat Morocco

On-board image processing represents a practical application for satellite imagery, aiming to optimize or prioritize data transmission to ground stations, thereby enhancing bandwidth utilization. In addition to their low cost and reduced development time, nanosatellites have experienced significant growth in applications and capabilities as technology advances. Presently, the implementation of on-board image processing has been successfully tested on real nanosatellite missions. Earth observation stands as the most utilized application for nanosatellites equipped with on-board imagers, and integrating image processing in those nanosatellites would enhance the overall performance of the mission. Hence, this article is a systematic review that investigates the current advancements and trends in terms of applications and technologies in the context of image processing on-board nanosatellites. To conduct the study systematically, Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were utilized. After the search and screening process, 73 relevant publications were analyzed to extract pertinent data for the review. The findings outlined a particular interest toward the application of deep learning methods on-board nanosatellites, especially for applications such as image classification or image segmentation.

关键词： Nanosatellites Earth observation image processing Systematic review PRISMA

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：