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Data-driven coordinated attention deep learning for high-fidelity brain imaging denoising and inpainting

JOURNAL OF BIOPHOTONICS 2024年第3期17卷 e202300390-e202300390页

作者： Luo, Chenggui Pang, Wen Shen, Binglin Zhao, Zewei Wang, Shiqi Hu, Rui Qu, Junle Gu, Bobo Liu, Liwei Shenzhen Univ Coll Phys & Optoelect Engn Key Lab Optoelect Devices & Syst Guangdong Prov & Minist Educ Shenzhen 518060 Peoples R China Shanghai Jiao Tong Univ Med X Res Inst Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai Peoples R China

deep learning offers promise in enhancing low-quality images by addressing weak fluorescence signals, especially in deep in vivo mouse brain imaging. However, current methods struggle with photon scarcity and noise within in vivo deep mouse brains, and often neglecting tissue preservation. In this study, we propose an innovative in vivo cortical fluorescence image restoration approach, combining signal enhancement, denoising, and inpainting. We curated a deep brain cortical image dataset and developed a novel deep brain coordinate attention restoration network (deepCAR), integrating coordinate attention with optimized residual networks. Our method swiftly and accurately restores deep cortex images exceeding 800 mu m, preserving small-scale tissue structures. It boosts the peak signal-to-noise ratio (PSNR) by 6.94 dB for weak signals and 11.22 dB for large noisy images. Crucially, we validate the effectiveness on external datasets with diverse noise distributions, structural features compared to those in our training data, showcasing real-time high-performance image restoration capabilities. This paper introduces an image restoration method proficient in denoising intense noise and amplifying weak photon-limited signals within in vivo mouse brain imaging. Meanwhile, it addresses imaging defects from fluorescent dyes in vascular imaging, preserving tissue structural ***

关键词： deep brain imaging deep learning image restoration multiphoton microscopy

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Robust image descriptor for machine learning based data reduction in serial crystallography

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JOURNAL OF APPLIED CRYSTALLOGRAPHY 2024年第2期57卷 413-430页

作者： Rahmani, Vahid Nawaz, Shah Pennicard, David Graafsma, Heinz Deutsch Elektronen Synchrotron DESY Notkestr 85 D-22607 Hamburg Germany Mid Sweden Univ Sundsvall Sweden

Serial crystallography experiments at synchrotron and X-ray free-electron laser (XFEL) sources are producing crystallographic data sets of ever-increasing volume. While these experiments have large data sets and high-frame-rate detectors (around 3520 frames per second), only a small percentage of the data are useful for downstream analysis. Thus, an efficient and real-time data classification pipeline is essential to differentiate reliably between useful and non-useful images, typically known as 'hit' and 'miss', respectively, and keep only hit images on disk for further analysis such as peak finding and indexing. While feature-point extraction is a key component of modern approaches to image classification, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. This paper proposes a pipeline to categorize the data, consisting of a real-time feature extraction algorithm called modified and parallelized FAST (MP-FAST), an image descriptor and a machine learning classifier. For parallelizing the primary operations of the proposed pipeline, central processing units, graphics processing units and field-programmable gate arrays are implemented and their performances compared. Finally, MP-FAST-based image classification is evaluated using a multi-layer perceptron on various data sets, including both synthetic and experimental data. This approach demonstrates superior performance compared with other feature extractors and classifiers.

关键词： serial crystallography data reduction machine learning feature extraction

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Video Analysis Method of Basketball Training Based on deep learning in the Context of Online Gaming

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Computer-Aided Design and Applications 2024年第S5期21卷 83-90页

作者： Qiushi, Li Henan Polytechnic Institute NanYang China

In order to tackle some issues of the inadequate data clustering in the original basketball shooting track capture and counter-capture method, a novel approach is proposed. This method utilizes the background differentiation technique for automated capture of basketball shooting trajectories. A sensor network is designed to capture the trajectory, ensuring improved accuracy and data integrity. The design process emphasizes the careful placement of sensors and the post-acquisition data processing. real-time and effective data acquisition is achieved through the use of inertial sensors. The trapper module handles data processing and trajectory capture. By employing the background differentiation technique, the target trajectory is detected and processed, enabling automatic capture of the shooting trajectory. This method significantly enhances track capture accuracy. Upon widespread adoption, it holds the potential to greatly improve the track capture impact in the field of basketball. © 2024 CAD Solutions,.

关键词： deep learning

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Optimizing deep learning for Diabetic Retinopathy Diagnosis

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第11期15卷 364-373页

作者： Sriporn, Krit Tsai, Cheng-Fa Rong, Li-Jia Wang, Paohsi Tsai, Tso-Yen Chen, Chih-Wen Suratthani Rajabhat Univ Dept Digital Technol Surat Thani Thailand Natl Pingtung Univ Sci & Technol Dept Management Informat Syst Pingtung Taiwan Cheng Shiu Univ Dept Food & Beverage Management Kaohsiung Taiwan Pingtung Cty Govt Chunri Township Publ Hlth Ctr Publ Hlth Bur Pingtung Taiwan Pingtung Christian Hosp Dept Emergency Med Pingtung Taiwan

The detection of diabetic retinopathy traditionally requires the expertise of medical professionals, making manual detection both time- and labor-intensive. To address these challenges, numerous studies in recent years have proposed automatic detection methods for diabetic retinopathy. This research focuses on applying deep learning and image processing techniques to overcome the issue of performance degradation in classification models caused by imbalanced diabetic retinopathy datasets. It presents an efficient deep learning model aimed at assisting clinicians and medical teams in diagnosing diabetic retinopathy more effectively. In this study, image processing techniques, including image enhancement, brightness correction, and contrast adjustment, are employed as preprocessing steps for fundus images of diabetic retinopathy. A fusion technique combining color space conversion, contrast limited adaptive histogram equalization, multi-scale retinex with color restoration, and Gamma correction is applied to highlight retinal pathological features. deep learning models such as ResNet50-V2, DenseNet121, Inception-V3, Xception, MobileNet-V2, and InceptionResNet-V2 were trained on the preprocessed datasets. For the APTOS-2019 dataset, DenseNet121 achieved the highest accuracy at 99% for detecting diabetic retinopathy. On the Messidor-2 dataset, InceptionResNet-V2 demonstrated the best performance, with an accuracy of 96%. The overall aim of this research is to develop a computer-aided diagnosis system for classifying diabetic retinopathy.

关键词： Diabetic retinopathy deep learning image processing technologies imbalanced image dataset computer aided diagnosis

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real-time assessment of ship collision risk using image processing techniques

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APPLIED OCEAN RESEARCH 2024年 153卷

作者： Ding, Haifeng Weng, Jinxian Shi, Kun Shanghai Maritime Univ Coll Transport & Commun Shanghai 201306 Peoples R China

The poor quality or the miss of Automatic Identification System (AIS) data may cause erroneous judgement of the potential navigational risk. Therefore, this study proposes a real-time framework for assessing ship collision risk using onboard video data in order to improve the risk perception ability of navigators. Firstly, the Squeeze-and- Excitation (SE) attention mechanism and the K-means algorithm are simultaneously utilized for the framework to enhance the multi-scale ship detection capability. The deep-SORT is employed to complete multi-ship feature matching. Secondly, the distances between two ships and their speeds are measured using the pinhole imaging principle based on the ship visual feature extraction results. Moreover, the ship distance-speed correction method is designed to improve the reliability of estimated results. Finally, the effectiveness of the framework is validated using naturalistic driving data from the "He Hua Hai" ship. The results show that the proposed framework could demonstrate an excellent performance in assessing ship collision risk using the onboard video data. The proposed framework could help precisely detect and promptly provide warnings about potential ship collision risks. This could help prevent catastrophic accidents that pose a threat to oceans and coasts, particularly in situations when AIS data proves to be unreliable or ineffective.

关键词： Maritime safety Collision risk Smart ship deep learning Computer vision

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Enhanced SCNN-Based Hybrid Spatial-Temporal Lane Detection Model for Intelligent Transportation Systems

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IEEE ACCESS 2024年 12卷 40075-40091页

作者： Li, Jingang Ma, Chenxu Han, Yonghua Mu, Haibo Jiang, Lurong Zhejiang Sci Tech Univ Sch Informat Sci & Engn Hangzhou 310018 Peoples R China Hangzhou Hikvis Digital Technol Co Ltd Hangzhou 310018 Peoples R China

Accurate and timely lane detection is imperative for the seamless operation of autonomous driving systems. In this study, leveraging the gradual variation of lane features within a defined range of width and length, we introduce an enhanced Spatial-Temporal Recurrent Neural Network (SCNN) framework. This framework serves as the cornerstone of an innovative hybrid spatial-temporal model for lane detection, which is tailored to address the prevalent issues of substandard detection performance and insufficient real-time processing in intricate scenarios, such as those involving lane erosion and inconsistent lighting conditions, which often challenge conventional models. With the foundational understanding that lanes manifest as continuous lines, we employ a temporal sequence of lane imagery as the input to our model, thereby ensuring a rich provision of feature information. The model adopts an encoder-decoder structure and integrates a Spatial-Temporal Recurrent Neural Network module for the extraction of interrelated information from the image sequence. The model culminates in the output of the lane detection results for the terminal frame. The proposed lane detection model exhibits a commendable synthesis of accuracy and real-time efficiency, attaining an Accuracy of 97.87%, an F-1 -score of 0.943, and a FPS of 19.342 on the tvtLANE dataset and an Accuracy of 98.21%, an F-1 -score of 0.957 on the Tusimple dataset. These metrics signify a superior performance over a majority of the current lane detection methods.

关键词： Computer vision deep learning Lane detection Autonomous driving Hybrid power systems Spatiotemporal phenomena Intelligent transportation systems Recurrent neural networks deep learning lane detection

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A novel Nine-SequenceNet with attention for liver histopathological classification

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BIOMEDICAL SIGNAL processing AND CONTROL 2024年 92卷

作者： Sun, Lin Sun, Zhanquan Wang, Chaoli Cheng, Shuqun Wang, Kang Univ Shanghai Sci & Technol Inst Opt Elect & Comp Engn Shanghai Peoples R China Second Mil Med Univ Shanghai Eastern Hepatobiliary Surg Hosp Dept Hepat Surg Shanghai Peoples R China

Liver cancer is a prevalent cancer worldwide and is also the leading cause of cancer-related deaths. Histopathological image diagnosis is taken as the standard to identify liver cancer. However, manually histopathological examination of liver cancer WSIs (Whole Slide image) is time consuming. To address this issue, researchers have applied the automatic classification methods to recognize cancerous tissues in histopathological images, which can effectively reduce the examination time and improving the recognition efficiency. In recent years, deep learning based methods have been widely used in histopathological image analysis because of their impressive performance level. But only a little research work based on transfer learning has been done on liver cancer histopathological images because the sample dataset is difficult to collected and the classification results lack of effectiveness and pertinence. One issue with fine-grained classification is that using a single patch for classification results in the loss of edge information, degrading classification performance. To solve the problem, this paper proposes a novel deep learning model based on sequence prediction with attention, which is used to identify and classify liver cancer histopathological images. The proposed method uses the surrounding 8 patches to supplement information to the current patch, resulting in better results than other methods. The experimental results show that this method achieves the best F1-score of 95.23% which illustrates that the proposed method is efficient in liver cancer histopathological image classification.

关键词： Liver Cancer Histopathological image Classification deep learning Attention Mechanism

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real-time Intrusion Detection in Power Grids Using deep learning: Ensuring DPU Data Security

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第3期5卷 814-827页

作者： Xiao, Maoran Zhou, Qi Zhang, Zhen Yin, Junjie State Grid Jiangsu Electric Power Co Ltd. Limited Information and Telecommunication Branch Jiangsu Nanjing210000 China State Grid Jiangsu Electric Power Co Ltd. Wuxi Power Supply Branch Jiangsu Wuxi214000 China

deep learning technologies have revolutionized the management of energy, energy consumption, and data security within smart grids through non-intrusive load monitoring (NILM). This paper explores the use of deep learning for real-time intrusion detection in power grids with a primary focus on safeguarding the integrity and security of Data processing Units (DPUs). An evaluation of various machine learning models, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Decision Trees, and Random Forests, is conducted to detect various types of intrusions, including Fault, Injection, Masquerade, Normal, and Replay. Random Forest produced AUC values of 1.00 for all classes and an overall F1-score of 0.99 for all classes. The Decision Tree model also shows robust performance for detecting Fault and Injection intrusions (AUC = 0.98), with an overall F1-score of 0.94. However, the LDA and SVM models do not perform well in detecting Injection intrusions with overall F1-scores of 0.83 and 0.86. Advances in machine learning can be used to improve smart grid security, reliability, and efficiency, according to this study. These findings highlight the potential of advanced machine learning techniques to enhance smart grid reliability and efficiency. © 2024, Ital Publication. All rights reserved.

关键词： Data integrity

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Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

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Petroleum Science 2024年第1期21卷 641-653页

作者： Yun-Peng He Hai-Bo Cheng Peng Zeng Chuan-Zhi Zang Qing-Wei Dong Guang-Xi Wan Xiao-Ting Dong State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016LiaoningChina Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016LiaoningChina Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169LiaoningChina University of Chinese Academy of Sciences Beijing100049China School of Artifcial Intelligence Shenyang University of TechnologyShenyang 110870LiaoningChina

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well *** learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis ***,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational ***,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different ***,there is heterogeneity in field data,which can dramatically impair the diagnostic *** solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this ***,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC ***,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data ***,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the *** on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational *** the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

关键词： Sucker-rod pumping system Dynamometer card Working condition recognition deep learning time-frequency signature time-frequency signature matrix

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Parallel Architecture for Low Latency UAV Detection and Tracking Using Robotic Telescopes

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第4期60卷 5515-5524页

作者： Ojdanic, Denis Naverschnigg, Christopher Sinn, Andreas Zelinskyi, Daniil Schitter, Georg TU Wien Automat & Control Inst ACIN A-1040 Vienna Austria

This article presents the implementation of a multithreaded parallel architecture, which enables telescope-based optical unmanned aerial vehicle (UAV) detection and tracking in real time. For efficient image processing an accurate deep learning object detector is complemented in parallel by a fast object tracker. A transition strategy between detector and tracker is introduced based on the tracker reliability, which improves the object localization accuracy of the system. The deep learning algorithm initializes the tracker and in the subsequent frames the reliability of the tracker is compared to the confidence value of each newly detected object to determine whether a reinitialization is necessary. The implemented architecture successfully demonstrates the parallel combination of an FRCNN detector and a MEDIANFLOW tracker to achieve visual UAV detection and tracking at 100 fps. The proposed reliability-based strategy outperforms a purely detector and tracker-based strategy by 6% and 14%, respectively, in terms of intersection over union at a threshold of 0.5, in scenarios, when the target UAV is flying in front of a complex background. In addition, the implemented parallel architecture increases the probability for a flight path estimation, which requires at least two localizations, by 49%, when compared to a nonparallel architecture. Field tests are conducted with the proposed architecture using a telescope system demonstrating UAV detection and tracking at 100 fps in distances up to 4000 m in front of a clear background.

关键词： Detectors Autonomous aerial vehicles Reliability deep learning Radar tracking Telescopes Cameras detection parallel tracking unmanned aerial vehicle (UAV)

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