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检索条件"任意字段=Conference on Physiology and Function from Multidimensional Images"

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3D Cellular Segmentation of Live Embryos via Topologically and Biologically Boundary-aware Semi-supervised Learning

3D Cellular Segmentation of Live Embryos via Topologically a...

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2024 IEEE International conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Zelin Huang, Zhaoke Yan, Hong City University of Hong Kong Department of Electrical Engineering Hong Kong Centre for Intelligent Multidimensional Data Analysis Hong Kong

ISBN: (纸本)9781665410205

3D cellular segmentation of fluorescence images of live embryos is a fundamental step in the analysis of embryonic developmental processes. However, existing fully supervised learning methods (CNN-based) to achieve this often suffer from non-robust loss functions and insufficient training data. Previous work on cellular segmentation did not consider topological information and biological constraints. In this paper, we propose a novel semi-supervised method using topological loss and biologically boundary-aware synthetic (labeled and unlabeled mixed) ground truth for 3D cellular segmentation of live embryos. The topological loss function guides the model to extract features in latent space. Semi-supervised learning and synthetic datasets improve the accuracy of inner and outer membrane recognition on a large number of unlabeled images. Experiment results and evaluation on 1472 live embryo images show that our method outperforms existing deep-learning models. This method can be adapted to images of live embryos of other animals. © 2024 IEEE.

关键词： Self-supervised learning

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3D-CA Net: A 3D-CNN with composite multidimensional attention for the assessment of lung aging 3

3D-CA Net: A 3D-CNN with composite multidimensional attentio...

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2024 3rd International conference on Image Processing, Object Detection, and Tracking, IPODT 2024

作者： Tian, Tangbin Pan, Jingshan Ge, Jing Li, Na Yu, Dexin Zuo, Liping Hu, Tao Liu, Shengyu Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Radiology Qilu Hospital of Shandong University Jinan China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

As the population ages, early diagnosis and treatment of lung diseases become increasingly important. Accurate assessment of aging-related changes in lung CT images is crucial for the prevention and treatment of related diseases. Traditional methods for lung aging assessment from CT images are time-consuming, subjective, and heavily reliant on the clinical experience of doctors. To address these issues, this paper proposes a lung aging assessment method with 3D-CA Net. The feature extraction part of the proposed network consists of four main 3D Convolutional and Composite multidimensional Attention Modules. By introducing the Composite multidimensional Attention Module, the advantages of spatial attention and self-attention are both utilized. Additionally, an improved E-cross-entropy loss function is employed to reduce overfitting and enhance generalization. Experimental results demonstrate that the 3D-CA Net significantly outperforms existing methods in terms of accuracy, macro-averaged precision, macro-averaged recall and macro-averaged F1 score. This work provides a comprehensive solution for lung CT image aging assessment and offers insights for future advancements in medical imaging analysis. © 2024 SPIE.

关键词： Lung cancer

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International Workshop on New Approaches for multidimensional Signal Processing, NAMSP 2023

International Workshop on New Approaches for Multidimensiona...

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International Workshop on New Approaches for multidimensional Signal Processing, NAMSP 2023

ISBN: (纸本)9789819727438

The proceedings contain 25 papers. The special focus in this conference is on New Approaches for multidimensional Signal Processing. The topics include: Weighted Tensor Least Angle Regression for Solving Sparse Weighted Multilinear Least Squares Problems;research on Behavior Control Method in 3D Virtual Animation Design Based on the Purpose of Improving the Effect of Overseas Dissemination;the Positive Exertion of "Fuzzy Control" in Art Appreciation Class;discussion on the Establishment and Application of Intelligent Design Platform for Concrete Proportioning;locally Adaptive Processing of Color Tensor images Represented as Vector Fields;Energy Efficient VgSOT-MTJ Based 1 Bit Subtractor;Hybrid Prediction Model for Mechanical Properties of Low Alloy Steel Based on SVR-MLP;A Human-Inspired Semantic SLAM Based on Parking-Slot Number for Autonomous Valet Parking;review of the Security Risks and Practical Concerns with Current and Future (6G) Communications Technology;effect of Rehabilitation Robot Training on Cognitive function in Stroke Patients: A Systematic Review and Meta-analysis;The Application Value of Virtual Reality Navigation Combined with Rapid On-Site Evaluation in CT-Guided Lung Biopsy;Gray and White Matters Segmentation in Brain CT images Using Multi-task Learning from Paired CT and MR images;wearable Long-Term Graph Learning for Non-invasive Mental Health Evaluation;music Personalized Recommendation System Based on Deep Learning;handwritten Mathematic Expression Conversion to Docx;application of Image Processing in Air-Ground Combined Fire Fighting System;Design and Realization of Mobile Terminal Side Time Synchronization Based on FPGA;exploration of Drone Trajectory Planning in Unknown Environments Using Reinforcement Learning;an Image-Content-Based Adaptive Tile Partitioning Algorithm.

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A multidimensional Scoliosis Assessment Algorithm Based on Back images 7

A Multidimensional Scoliosis Assessment Algorithm Based on B...

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7th International conference on Biological Information and Biomedical Engineering, BIBE 2024

作者： Peng, Xiangwei Sun, Jingwei Xie, Ruimou Pan, Yu Ni, Xueyi Luo, Zhendong International Graduate School Tsinghua University Shenzhen China Department of Rehabilitation Medicine Beijing Tsinghua Changgung Hospital Beijing China Department of Radiology The University of Hong Kong Shenzhen Hospital Shenzhen China

ISBN: (纸本)9783800762910

Scoliosis refers to the deviation of the spine from the centerline of the torso on one or both sides, forming a "C" or "S" shaped curvature. The rehabilitation period for scoliosis is long and requires regular follow-up. If the patient is frequently exposed to X-rays, it can cause irreversible damage to the blood and reproductive systems. The main research work of this article is as follows:1. Research on monocular ranging algorithm: In response to the problem that the scoliosis assessment module need to measure the distance between the camera and the human body, a technology using a common camera combined with an object detection algorithm is proposed, which can achieve monocular ranging without the need for a depth camera. The relative ranging error is less than 10%, providing quality control function for input images for subsequent scoliosis assessment task. 2. Research on multidimensional Assessment Algorithms for Scoliosis: In response to the problem of previous studies only focusing on AI assessment of the coronal plane of the spine, this study designed and implemented: (1) an angle of trunk rotation measurement algorithm for assessment the degree of spinal deviation in the sagittal plane, with a relative measurement error of 6.2%;(2) The spinal curvature type discrimination algorithm used to determine whether the spine is in a "C", "S", or multi curvature shape has a discrimination accuracy of 0.887;(3) The spinal coronal scoliosis grading algorithm, which combines doctor's assessment experience and is used to evaluate the severity of the spine on the coronal plane, has a grading accuracy of 0.793, higher than existing methods. This algorithm solves the problem of patients having difficulty performing spinal dynamic assessment at home. © VDE VERLAG GMBH.

关键词： Patient rehabilitation

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Violent behavior detection algorithm in aerial images based on YOLOv9

Violent behavior detection algorithm in aerial images based ...

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2024 International conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2024

作者： Han, Baixuan Peng, Yueping Hao, Hexiang Yin, Wenji Liu, Wenchao School of Information Engineering Engineering University of PAP Xi’an710086 China

ISBN: (纸本)9781510685840

Aiming at the shortage of accuracy and feature extraction ability of aerial image behavior detection algorithm, we designed an improved YOLOv9 fight behavior detection algorithm. WE use drones to take aerial photos and build datasets of violent behavior, and we add multidimensional Collaborative Attention (MCA) to the object detection header to improve the detection accuracy, and introduce maximum feature pooling to strengthen the ability of network feature extraction for self-built datasets. For unevenly distributed data sets, Focaler-loss function is used. The results show that compared with the original network, the detection accuracy is improved from 90.6% to 92.9%, and the FPS reaches 56 frames per second, which meets the requirement of high precision real-time detection. © 2024 SPIE ·

关键词： Drones

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A Transformer-based Method for Skin Fungi Identification from Fluorescent images 23

A Transformer-based Method for Skin Fungi Identification fro...

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2023 8th International conference on Biomedical Signal and Image Processing, ICBIP 2023

作者： Yuan, Yutong Dai, Sizhe Wang, Ziyi Zhu, Min Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China Department of Dermatology Huashan Hospital Fudan University Shanghai2000240 China

ISBN: (纸本)9798400707698

Due to the increasing number of patients with impaired immune function and the widespread use of broad-spectrum antibiotics, the rate of fungal infections is rising and invasive fungal infections (IFD) have become one of the major causes of death. Therefore, early diagnosis and treatment of IFD is the focus of medical attention. In order to automatically identify and diagnose the fungal species of infected patients, we propose a fluorescent identification method of skin fungi with a compact model and high identification accuracy. The method uses fluorescence staining to label fungi, and data enhancement processing is applied to the collected sample images to compensate for the shortcomings of less clinical data of some fungal samples. Convolutional Tokenizer and Positional Embedding are applied to provide sample image labeling in the recognition process, and Transformer is used to solve the problem of too many layers of traditional Convolutional Neural Networks and expand the image perceptual field;In the Sequence Pooling module, the attention mechanism is used to pool all the tokens. Finally, it is transformed into the final category output through a Fully Connected layer. The experimental results show that the method takes less training time, has a compact model and a smaller number of parameters, while still maintaining a high recognition accuracy, which is of great practical value in clinical diagnosis. © 2023 ACM.

关键词： Fungi

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Dock Detection Method in Remote Sensing images Based on Improved YOLOv4 14

Dock Detection Method in Remote Sensing Images Based on Impr...

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14th International conference on Digital Image Processing, ICDIP 2022

作者： Guo, Haitao Gao, Hui Guo, Chao Lu, Jun Lin, Yuzhun Strategic Support Force Information Engineering University Zhengzhou450001 China Nanjing Dizheng Information Technology Co. LTD. Luoyang Branch Luoyang471000 China

ISBN: (纸本)9781510657564

The dock target in remote sensing images has the characteristics of slender structure and direction arbitrarily. The general target detection algorithm based on the convolutional neural network cannot effectively obtain the direction information of the target, which cannot meet the actual demand of dock detection. This study designed a deep convolutional neural network architecture in any direction based on the YOLOv4 algorithm aimed at resolving the above problems. First, the multidimensional coordinate method was used to calibrate the dock target so that the network could contain the direction information of the target. Second, the loss function of the algorithm was optimized to make it suitable for directional target detection. Finally, an attention mechanism was introduced to enhance the extraction ability of the algorithm and further improve its detection accuracy. Two datasets of dock target detection from remote sensing images were selected for experiments, and the results showed that the improved YOLOv4 network was better than the other networks in the dock target detection task. © 2022 SPIE.

关键词： Image enhancement

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An autofocus algorithm for microscopic hyperspectral imaging system with adaptive wavelength variation 22

An autofocus algorithm for microscopic hyperspectral imaging...

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14th International conference on Bioinformatics and Biomedical Technology, ICBBT 2022

作者： Sun, Xingyu Wang, Yan Li, Qingli Shanghai Key Laboratory of Multidimensional Processing East China Normal University Shanghai200241 China East China Normal University Shanghai200241 China

ISBN: (纸本)9781450396387

Microscopic hyperspectral imaging techniques have been widely used to analyze information from digital pathology sections. Real-time autofocusing is one of the important techniques to acquire high-quality images efficiently;however, there are few autofocusing algorithms are proposed in this field. Therefore, this article proposes an autofocus algorithm for a self-developed microscopic hyperspectral imaging system. This method can finish autofocus with a single field of view in 0.9 seconds. In addition, to reduce the focusing time, we fit a wavelength as a function of focal length. We applied the above method on our own dataset and the experimental results show that this method can acquire large-scale microscopic hyperspectral images quickly and accurately. © 2022 ACM.

关键词： Hyperspectral imaging

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A Rapid, and Low-Cost Electrical Impedance Tomography Sensor System for Non-Destructive Evaluation of Multiple Inhomogeneities

A Rapid, and Low-Cost Electrical Impedance Tomography Sensor...

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Electrical, Computer and Energy Technologies (ICECET), International conference on

作者： Rinku Basak Department of Electrical and Electronic Engineering American International University-Bangladesh Dhaka Bangladesh

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Image reconstruction of the inhomogeneities is very important to evaluate the characteristics by creating multidimensional images using non-destructive measurements. The reconstructed image with the lowest possible noise is desirable in order to maintain a good resolution and high accuracy. This paper presents an approach to reconstruct and evaluate the multiple inhomogeneities by utilizing non-invasive and non-destructive measurements considering an Electrical Impedance Tomography $(\text{EIT})$ sensor system. A high-precision sensor using an array of electrodes in a cylindrical structured domain is designed for multidimensional measurements of non-conductive, bio-, and conductive inhomogeneities. The impedances at frequencies up to 100 $\text{kHz}$ are measured and the accuracy is evaluated. The reconstruction analysis is performed successfully using a complete electrode model with the help of impedance difference method considering finite element method (FEM) modeling. A noise controlled hyperparameter value ranges from 0.0097 to 0.57 in addition with the type, position, and size of the inhomogeneity is considered in the analysis.

关键词： Electrodes Electrical impedance tomography Impedance measurement Noise Nonhomogeneous media Sensor systems physiology Frequency measurement Impedance Image reconstruction

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3D Cellular Segmentation of Live Embryos via Topologically and Biologically Boundary-aware Semi-supervised Learning

3D Cellular Segmentation of Live Embryos via Topologically a...

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IEEE International conference on Systems, Man and Cybernetics

作者： Zelin Li Zhaoke Huang Hong Yan Department of Electrical Engineering City University of Hong Kong Hong Kong SAR China Centre for Intelligent Multidimensional Data Analysis Hong Kong SAR China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

关键词： Image segmentation Adaptation models Embryo Three-dimensional displays Biological system modeling Artificial neural networks Semisupervised learning Feature extraction Transformers Synthetic data

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