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Discovering the nuclear localization signal universe through a deep learning model with interpretable attention units

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patterns 2025年第6期6卷

作者： Li, Yi-Fan Pan, Xiaoyong Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

We describe NLSExplorer, an interpretable approach for nuclear localization signal (NLS) prediction. By utilizing the extracted information on nuclear-specific sites from the protein language model to assist in NLS detection, NLSExplorer achieves superior performance with greater than 10% improvement in the F1 score compared with existing methods on benchmark datasets and highlights other nuclear transport segments. We applied NLSExplorer to the nucleus-localized proteins in the Swiss-Prot database to extract valuable segments. A comprehensive analysis of these segments revealed a potential NLS landscape and uncovered features of nuclear transport segments across 416 species. This study introduces a powerful tool for exploring the NLS universe and provides a versatile network that can efficiently detect characteristic domains and motifs. © 2025 The Author(s)

关键词： Deep learning

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Accurate extraction of line-structured optical stripe centerlines under low exposure 12

Accurate extraction of line-structured optical stripe center...

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SPIE 12th International Symposium on Multispectral Image processing and pattern recognition, MIPPR 2023

作者： Zhu, Cheng Tian, Keyun Gao, Yao Ding, Zhiqiang Hubei Key Laboratory of Optical Information and Pattern Recognition China Hubei Engineering Research Center of Video Image and HD Projection China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

The line-structure light bar images acquired in industrial metal parts inspection may suffer from inaccurate extraction of the centerline of the line structure light bar due to the interference of ambient lighting and strong diffuse reflection and weak reflection of the part surface material. For this reason, this study proposes a method based on G-HL (Gauss-Hyper-Laplace Distribution) curve fitting to improve the center extraction accuracy of the line structure light bar in complex environments. First, the method uses Gaussian filtering to denoise the line-structured light image. Then, the laser line target pixel region is localized by finding the upper and lower boundaries of the line-structured light bar using the neighborhood gradient for each column of the line-structured light image, and the width of the line-structured light bar is calculated by the difference of the boundary coordinates, and the fitting interval is determined. Finally, within the fitting interval, the G-HL curve is used to fit the gray scale of the line-structured light cross-section to obtain the G-HL parameter, and the point of the maximum value of the G-HL curve is taken to be the coordinates of the center of the line-structured light. Experiments prove that the proposed method can accurately extract the center of the line structure light in low exposure. Compared with the methods such as the grayscale center of gravity method and the extreme value method, the proposed method has high accuracy and robustness. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Curve fitting

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Dense Center Prediction for Centerline Extraction Based on BP Network 12

Dense Center Prediction for Centerline Extraction Based on B...

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SPIE 12th International Symposium on Multispectral Image processing and pattern recognition, MIPPR 2023

作者： Gui, Chuanqi Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan430205 China Hubei Engineering Research Center of Video Image and HD Projection Wuhan430205 China School of Electrical and Information Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

In the line structured light three-dimensional measurement system, high-precision laser stripe centerline extraction is the key to improving measurement accuracy. The laser line extraction technology based on neural network can automatically detect and extract laser lines with high accuracy and robustness. In this paper, a BP neural network containing one hidden layers is constructed, and the centerline obtained through feature extraction of the network can effectively overcome the influence of noise interference. Compared with the gray-gravity method and the Steger algorithm, the method in this paper has the advantage of high accuracy and can meet the requirements for extracting the center of complex light strips. © 2024 SPIE.

关键词： Extraction

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Generation of six-lobe photoelectron angular distribution via three-leaf clover laser field

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Optical and Quantum Electronics 2024年第11期56卷 1-11页

作者： Xu, Shengliang Huang, Liang Zhang, Xiaodong Kuang, Jidi Hong, Zuofei Wang, Run GAC Automotive Research & amp Development Center Guangzhou China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan China College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua China

We theoretically investigate the low-energy photoelectron angular distribution (PAD) during ionization of the P± atomic orbitals by counter-rotating two-color circularly polarized laser fields. Our study reveals a transition from three-lobe to six-lobe structure in the low-energy PAD for P+ orbital when the intensity ratio between the co-rotating 800-nm (ω0) and counter-rotating 400-nm (2ω0) laser components is increased, while the three-lobe structure is maintained for the P- orbital. The three-lobe structure is exclusively attributed to the (6×2ω0-1×ω0) pathway, whereas the (5×2ω0+1×ω0) pathway begins to dominate for the six-lobe structure. These results clearly and unambiguously demonstrate that the PAD in counter-rotating laser fields can be utilized for the detection of P+ and P- orbitals and also offer additional degrees of freedom for precisely controlling the contributions of various ionization pathways. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Angular distribution

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A Robust Algorithm for Reading recognition of Pointer Instrument with Local Matching 12

A Robust Algorithm for Reading Recognition of Pointer Instru...

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SPIE 12th International Symposium on Multispectral Image processing and pattern recognition, MIPPR 2023

作者： Niu, Tun Ye, Liang Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan430205 China Hubei Engineering Research Center of Video Image and HD Projection Wuhan430205 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

The inspection and recognition of readings on pointer instruments are important tasks in power inspection. Due to the diversity and the complex dial information of pointer instruments, the adaptability and low accuracy of existing methods are not enough. In this paper, we proposed a pointer instrument reading recognition algorithm based on local fast matching. Firstly, YOLOv8 is used to locate and segment the instrument in the inspection image. The positions and values of key scale are marked in the template image. Based on the key local information, a mapping relationship is established between the instrument location region in the inspection image and the template image. Then, the Hough transform is used to detect the position of the pointer, map it to the template image, and finally calculate the instrument reading based on the relative positions of the key graduations on the template image and the mapped pointer position. Experimental results demonstrate that the proposed method achieves a relative error within 5% for pointer instrument reading recognition in indoor substations. © 2024 SPIE.

关键词： Inspection

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Deep Learning Based Neovascularization Detection in Color Fundus Photography Image 23

Deep Learning Based Neovascularization Detection in Color Fu...

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9th International Conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Guo, Tianjiao Yang, Jie Yu, Qi Institute of Medical Robotics Institute of Image Processing and Pattern Recognition Department of Automation School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Shanghai General Hospital Shanghai Jiao Tong University School of Medicine Shanghai China National Clinical Research Center for Eye Diseases Shanghai China

ISBN: (纸本)9781450399029

Diabetic retinopathy (DR) has already been one of the leading causes of vision loss. A large number of researches about deep learning-based DR screening using color retinal photography images have been proposed in recent years. However, existing works mainly concentrated on non-proliferative diabetic retinopathy (NPDR) lesions. The exploration of proliferative diabetic retinopathy (PDR) lesion, which is more serious, is still insufficient. In this paper, we explored into one typical PDR lesion named neovascularization (NV). We collected and annotated retinal images from public and private datasets to construct and release a new dataset for NV detection. Three tasks including segmentation, detection, and grading were conducted on our dataset by using a set of state-of-the-art deep learning models. The results show that introducing NV detection can benefit DR grading. However, the segmentation and detection of NV are still challenging and have considerable room for improvement. © 2023 Copyright held by the owner/author(s).

关键词： Color

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Accurate Laser Line Extraction Algorithm Based on Morphological Features under Strong Interference 12

Accurate Laser Line Extraction Algorithm Based on Morphologi...

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SPIE 12th International Symposium on Multispectral Image processing and pattern recognition, MIPPR 2023

作者： Wu, Yuanzhe Ye, Liang Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan430205 China Hubei Engineering Research Center of Video Image and HD Projection Wuhan430205 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

In outdoor complex lighting environments, laser stripe extraction becomes challenging due to the possibility of strong exposures in the external environment as well as reflective interference on the road surface. In order to overcome these problems, this paper proposes a multi-feature structured light extraction algorithm based on seed neighborhoods, which utilizes multiple seed points and comprehensively considers the laser stripe features, especially the morphological features of the optical stripes in their surrounding neighborhoods, so as to obtain the approximate distribution location of the real laser stripe. Finally, the laser streak extraction is performed by the gradient difference of the optical streak boundary as well as its own brightness. The experimental results show that the method is still effective in extracting laser lines even in high exposure environments where there are interferences such as reflections on the road surface. © 2024 SPIE.

关键词： Luminance

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An Improved Watershed Foreign Object Detection of Road Surface Based on Adaptive Gaussian Threshold 12

An Improved Watershed Foreign Object Detection of Road Surfa...

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SPIE 12th International Symposium on Multispectral Image processing and pattern recognition, MIPPR 2023

作者： Liang, Yongjie Tian, Keyun Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan430205 China Hubei Engineering Research Center of Video Image and HD Projection Wuhan430205 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

To a certain extent, foreign objects on road surfaces can cause damage to vehicles and increase the risk of accidents, which is even more immeasurably harmful in such special scenarios as airports. Effective detection of foreign objects on road surfaces is of great significance to airport flight safety and road safety maintenance. Due to the traditional watershed segmentation method in the recognition of foreign objects, there are obvious over-segmentation, noise complexity and so on. Therefore, this paper proposes an improved watershed segmentation method based on Lab-color space using image filtering techniques and morphological operations to initially separate the image foreground and background, combined with Adaptive Gaussian thresholding, labeling method for detection. This effectively solves the problem of over-segmentation, can better suppress image noise, and accurately detects foreign objects on road surfaces. © 2024 SPIE.

关键词： Object detection

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An Improved Bilateral Filtering Based on Curvature Feature for Point Cloud Denoising in Road Surface Detection 12

An Improved Bilateral Filtering Based on Curvature Feature f...

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SPIE 12th International Symposium on Multispectral Image processing and pattern recognition, MIPPR 2023

作者： Chen, Si Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan430205 China Hubei Engineering Research Center of Video Image and HD Projection Wuhan430205 China School of Electrical and Information Technology Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

To effectively remove noise from the point clouds obtained for road surface defect detection while preserving the feature information, a bilateral filtering algorithm based on curvature features is proposed for denoising road surface defect point clouds. First, divide the noise points in the point cloud into several regions based on their distribution characteristics and curvature features. Then use statistical filtering to remove noise points in the obvious noise area and the flat area. Finally, an improved bilateral filtering algorithm is applied to smooth the noise points in the complex detail area. © 2024 SPIE.

关键词： Surface defects

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GRC-Net: Grouping Guided R-band Chromosome recognition Network 24

GRC-Net: Grouping Guided R-band Chromosome Recognition Netwo...

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10th International Conference on Computing and Artificial Intelligence, ICCAI 2024

作者： Xia, Chao Liu, Wei Liu, Zhao Jiang Chen, Bing Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Shanghai Institute of Hematology State Key Laboratory of Medical Genomics National Research Center for Translational Medicine at Shanghai Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China

ISBN: (纸本)9798400717055

Chromosome recognition is a critical and time-consuming process in karyotyping, especially for R-band chromosomes with poor visualization quality. In this paper, we propose an end-to-end grouping guided R-band chromosome recognition method GRC-Net. GRC-Net serves the chromosome recognition task as the main task and takes the chromosome length grouping task and centromere position grouping task as auxiliary tasks. Two auxiliary modules, Chromosome Length Grouping Module (CLGM) and Centromere Position Grouping Model (CPGM), are designed to extract the task-specific feature and refine the feature map of the main task. A large-scale R-band chromosome dataset with 1735 cases was collected. Experiment results on the 423 testing cases show that the proposed GRC-Net gets the highest accuracy of 96.87%, outperforming the baseline by 2.24%. With grouping task-guided feature extraction, GRC-Net reduces approximately 50% of the inter-group misclassification. The proposed GRC-Net can serve as a general framework for incorporating the domain knowledge into the process of feature learning, meanwhile, a powerful tool to assist clinical chromosome karyotyping. © 2024 ACM.

关键词： Feature extraction

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