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Applications of artificial intelligence to lipid nanoparticle delivery

Particuology 2024年第7期90卷 88-97页

作者： Ye Yuan Yuqi Wu Jiabei Cheng Kaiyuan Yang Yilin Xia Hongguang Wu Xiaoyong Pan Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education of ChinaChina Songjiang Research Institute Songjiang District Central HospitalShanghai Jiao Tong University School of MedicineShanghaiChina

Lipid nanoparticles(LNPs)are nanocarriers composed of four lipid components and can be used for gene therapy,protein replacement,and vaccine ***,LNPs also face several challenges,such as toxicity,immune activation,and low delivery *** overcome these challenges,artificial intelligence can be used to optimize the design and formulation of LNPs,as well as to predict their properties and ***,antibody-targeted conjugation can be used to enhance the specificity and selectivity of LNPs by attaching an antibody that recognizes a specific antigen on the cell surface to LNPs.

关键词： Artificial intelligence Lipid nanoparticles Antibody Targeted conjugation

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FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud 26

FASTC: A Fast Attentional Framework for Semantic Traversabil...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Chen, Yirui Wei, Pengjin Liu, Zhenhuan Wang, Bingchao Yang, Jie Liu, Wei Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education China

ISBN: (纸本)9781643684369

Producing traversability maps and understanding the surroundings are crucial prerequisites for autonomous navigation. In this paper, we address the problem of traversability assessment using point clouds. We propose a novel pillar feature extraction module that utilizes PointNet to capture features from point clouds organized in vertical volume and a 2D encoder-decoder structure to conduct traversability classification instead of the widely used 3D convolutions. This results in less computational cost while even better performance is achieved at the same time. We then propose a new spatio-temporal attention module to fuse multi-frame information, which can properly handle the varying density problem of LIDAR point clouds, and this makes our module able to assess distant areas more accurately. Comprehensive experimental results on augmented Semantic KITTI and RELLIS-3D datasets show that our method is able to achieve superior performance over existing approaches both quantitatively and quantitatively. Our code is publicly available at https://***/chenyirui/FASTC. © 2023 The Authors.

关键词： Semantics

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ChromTR:chromosome detection in raw metaphase cell images via deformable transformers

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Frontiers of Medicine 2024年第6期18卷 1100-1114页

作者： Chao Xia Jiyue Wang Xin You Yaling Fan Bing Chen Saijuan Chen Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong UniversityShanghai200240China Shanghai Institute of Hematology State Key Laboratory of Medical GenomicsNational Research Center for Translational Medicine at ShanghaiRuijin Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai200025China

Chromosome karyotyping is a critical way to diagnose various hematological malignancies and genetic diseases,of which chromosome detection in raw metaphase cell images is the most critical and challenging *** this work,focusing on the joint optimization of chromosome localization and classification,we propose ChromTR to accurately detect and classify 24 classes of chromosomes in raw metaphase cell *** incorporates semantic feature learning and class distribution learning into a unified DETR-based detection ***,we first propose a Semantic Feature Learning Network(SFLN)for semantic feature extraction and chromosome foreground region segmentation with object-wise ***,we construct a Semantic-Aware Transformer(SAT)with two parallel encoders and a Semantic-Aware decoder to integrate global visual and semantic *** provide a prediction with a precise chromosome number and category distribution,a Category Distribution Reasoning Module(CDRM)is built for foreground-background objects and chromosome class distribution *** evaluate ChromTR on 1404 newly collected R-band metaphase images and the public G-band dataset *** proposed ChromTR outperforms all previous chromosome detection methods with an average precision of 92.56%in R-band chromosome detection,surpassing the baseline method by 3.02%.In a clinical test,ChromTR is also confident in tackling normal and numerically abnormal *** extended to the chromosome enumeration task,ChromTR also demonstrates state-of-the-art performances on R-band and G-band two metaphase image *** these superior performances to other methods,our proposed method has been applied to assist clinical karyotype diagnosis.

关键词： chromosome detection deformable transformer metaphase cell image distribution reasoning

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Epitaxial growth of 2D gallium selenide flakes for strong nonlinear optical response and visible-light photodetection

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Frontiers of physics 2023年第5期18卷 237-244页

作者： Mengting Song Nan An Yuke Zou Yue Zhang Wenjuan Huang Huayi Hou Xiangbai Chen Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of TechnologyWuhan 430205China State Key Laboratory of Materials Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China

As an emerging groupⅢ–Ⅵsemiconductor two-dimensional(2D)material,gallium selenide(GaSe)has attracted much attention due to its excellent optical and electrical *** this work,high-quality epitaxial growth of few-layer GaSe nanoflakes with different thickness is achieved via chemical vapor deposition(CVD)*** to the non-centrosymmetric structure,the grown GaSe nanoflakes exhibits excellent second harmonic generation(SHG).In addition,the constructed GaSe nanoflake-based photodetector exhibits stable and fast response under visible light excitation,with a rise time of 6 ms and decay time of 10 *** achievements clearly demonstrate the possibility of using GaSe nanoflake in the applications of nonlinear optics and(opto)-electronics.

关键词： 2D materials gallium selenide second harmonic generation chemical vapor deposition photodetector

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GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

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Computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Global Institute of Future Technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

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A geographical and operational deep graph convolutional approach for flight delay prediction

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Chinese Journal of Aeronautics 2023年第3期36卷 357-367页

作者： Kaiquan CAI Yue LI Yongwen ZHU Quan FANG Yang YANG Wenbo DU School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Key Laboratory of National Airspace Technology Beijing 100085China National Lab of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China Research Institute for Frontier Science Beihang UniversityBeijing 100191China

Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning,flight scheduling,airport operation,and passenger *** delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity,which brings critical challenges to accurate flight delay *** recent years,Graph Convolutional Networks(GCNs)have become popular in flight delay prediction due to the advantage in extracting complicated ***,most of the existing GCN-based methods have failed to effectively capture the spatial-temporal information in flight delay *** this paper,a Geographical and Operational Graph Convolutional Network(GOGCN)is proposed for multi-airport flight delay *** GOGCN is a GCN-based spatial-temporal model that improves node feature representation ability with geographical and operational spatial-temporal interactions in a ***,an operational aggregator is designed to extract global operational information based on the graph structure,while a geographical aggregator is developed to capture the similar nature among spatially close *** experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement.

关键词： Flight delay prediction Flight operation pattern Geographical interactive information Graph neural network Spatial-temporal information

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An improved image segmentation algorithm based on MRF and Sobel operator 12

An improved image segmentation algorithm based on MRF and So...

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

作者： Gao, Yao Tian, Keyun Zhu, Cheng 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)9781510674943

ISBN: (纸本)9781510674936

In response to the issues of over-segmentation, excessive noise, and suboptimal segmentation results commonly encountered in existing image segmentation algorithms based on Markov Random Fields (MRF), this paper proposes an enhanced image segmentation algorithm that integrates MRF with the Sobel operator. The algorithm begins by performing an initial segmentation of the image using a Markov Random Field (MRF)-based method. Subsequently, an enhanced Sobel operator is employed to eliminate noise points and extract fine edge details from the image. Finally, the segmentation result is refined through pixel-wise operations with the edge detection result, resulting in the ultimate segmentation output. The evaluation of segmentation performance is conducted using the Dice coefficient and Mean Hausdorff Distance as assessment metrics. Through experimental analysis, the method in this paper can improve the segmentation effect of the traditional MRF segmentation algorithm, and has better performance and higher adaptivity. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image segmentation

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Photovoltaic panel string detection based on prior knowledge and feature learning 12

Photovoltaic panel string detection based on prior knowledge...

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

作者： Li, Bolin Cheng, Bolin Ye, Liang 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)9781510674943

ISBN: (纸本)9781510674936

Image-based photovoltaic panel inspection has become one of the important tasks of photovoltaic power generation. Due to the repeated patterns of photovoltaic string numbers and different specifications, it is easy to cause false detection by traditional template matching or model training methods. In order to solve this problem, this paper proposes a photovoltaic panel string detection method based on prior knowledge and feature learning. First, the infrared inspection image is segmented for the first time by using the difference in radiation characteristics between the photovoltaic string and the environmental background. Subsequently, according to the local features of the border around the PV string template image, feature matching is performed in the segmented foreground area, and then determine the exact position of the string through the closure and integrity of the matching area. Experiments show that this method has high detection accuracy and has good adaptability to complex and diverse string specifications. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Inspection

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Defect detection of photovoltaic panel based on morphological segmentation 12

Defect detection of photovoltaic panel based on morphologica...

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

作者： Cheng, Bolin Li, Bolin Ye, Liang 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)9781510674943

ISBN: (纸本)9781510674936

The automatic inspection of photovoltaic panels based on infrared images is one of the important tasks in the daily maintenance of photovoltaic panels in photovoltaic power plants. In this paper, a defect detection method of infrared thermal image photovoltaic panel based on morphological segmentation is proposed. First of all, according to the infrared characteristics of the photovoltaic plant station and the morphological characteristics of the photovoltaic panel, the accurate region of each photovoltaic panel is determined in the inspection image. Then, statistical method is used for feature analysis to determine whether there are defects in photovoltaic panels, and then the defective photovoltaic panels are segmented and different defects are classified according to morphological characteristics, and finally the defect detection and classification of photovoltaic panels are realized. Through the experiment in the actual inspection image of photovoltaic plant station, it is verified that the proposed method has a high accuracy of defect detection and recognition. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Morphology

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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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