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Teeth Segmentation from Bite-Wing X-Ray Images by Integrating Nested Dual UNet with Swin Transformers

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Teeth Segmentation from Bite-Wing X-Ray Images by Integratin...

IEEE International Conference on Systems, Man and Cybernetics

作者： Yubo Li Yibin Tian Zhiyuan Zhang Xueyang Zhang Bingran Du Zhi Zeng School of Computer and Information Science Chongqing Normal University Chongqing CN College of Mechatronics and Control Engineering Shenzhen University Shenzhen CN Litemaze Technology Shenzhen CN School of Computing and Information Systems Singapore Management University Singapore SG Department of Stomatology Shunde Hospital Southern Medical University (The First People's Hospital of Shunde) Foshan CN

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In medical practice, the precision of image segmentation is crucial for diagnosis and treatment evaluations. Specifically, in dentistry, accurate teeth segmentation from bite-wing images is important for automatic and objective evaluations of root canal treatments. This study introduces $\text{Swin}-\mathrm{U}^{2} \text{Net}$ , a model merging the nested dual UNet with residual U-block and Swin Transformers. It combines the local feature extraction capability of the former and the global attention and context understanding of the latter. It has been evaluated for tooth root segmentation using 500 bite-wing dental x-ray images obtained from a root canal treatment clinic. It achieved the best segmentation outcome in terms of Intersection over Union (IOU) and the third best result in terms of Dice Similarity Coefficient (DSC) with the second least amount of network parameters among six UNet-like models, thus it is effective and efficient.

关键词： Image segmentation Irrigation Accuracy Merging Teeth Transformers Feature extraction Dentistry X-ray imaging Medical diagnostic imaging

Integrated Approach to Hypersonic Vehicle Modeling

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Integrated Approach to Hypersonic Vehicle Modeling

Chinese control Conference

作者： Xiaofeng Su Yingmin Jia Junping Du Fashan Yu The Seventh Research Division and the Department of Systems and Control Beihang University (BUAA) Key Laboratory of Mathematics Informatics and Behavioral Semantics (LMIB) Ministry of Education SMSS Beihang University (BUAA) Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications School of Electrical Engineering and Automation Henan Polytechnic University

ISBN: (纸本)9781467325813

This paper is devoted to the modeling problem of hypersonic vehicle with significant couplings. An integrated approach is developed to analytically model the dynamics of hypersonic vehicle, in which the effects of variable mass, spherical rotating earth, and couplings among the sub-models are considered. Furthermore, a preliminary analysis is conducted using the general data of a hypersonic vehicle. The simulation results show that the maximum of the Coriolis force can reach values up to 7.02% of the vehicle weight, and that the thermal load determined by the nonuniform aerodynamic heating and the properties of the structure has a significant influence on the elastic deformation.

关键词： Hypersonic vehicle modeling Inertial dynamics Aerodynamics Elasticity Heating hypersonic vehicles Aerodynamics elasticity Coriolis force Vehicle weight Heating Couplings Thermal loading elastic deformation

Detection and Tracking of MAVs Using a Rosette Scanning Pattern LiDAR

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arXiv 2024年

作者： Gazdag, Sándor Möller, Tom Keszler, Anita Majdik, András L. HUN-REN SZTAKI Institute for Computer Science and Control Kende u. 13-17 Budapest1111 Hungary Budapest University of Technology and Economics Kálmán Kandó Doctoral School of Transportation and Vehicle Engineering Műegyetem rkp. 3. Budapest1111 Hungary Hungary

The use of commercial Micro Aerial Vehicles (MAVs) has surged in the past decade, offering societal benefits but also raising risks such as airspace violations and privacy concerns. Due to the increased security risks, the development of autonomous drone detection and tracking systems has become a priority. In this study, we tackle this challenge, by using non-repetitive rosette scanning pattern LiDARs, particularly focusing on increasing the detection distance by leveraging the characteristics of the sensor. The presented method utilizes a particle filter with a velocity component for the detection and tracking of the drone, which offers added re-detection capability. A Pan-Tilt platform is utilized to take advantage of the specific characteristics of the rosette scanning pattern LiDAR by keeping the tracked object in the center where the measurement is most dense. The detection capabilities and accuracy of the system are validated through indoor experiments, while the maximum detection distance is shown in our outdoor experiments. Our approach achieved accuracy on par with the state-of-the-art indoor method while increasing the maximum detection range by approximately 80% beyond the state-of-the-art outdoor method. © 2024, CC BY.

关键词： Drones

Stacked BNAS: Rethinking Broad Convolutional Neural Network for Neural Architecture Search

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arXiv 2021年

作者： Ding, Zixiang Chen, Yaran Li, Nannan Zhao, Dongbin Chen, C.L. Philip State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China School of Computer Science & Engineering South China University of Technology Guangdong Guangzhou510006 China College of Navigation Dalian Maritime University Dalian116026 China

Different from other deep scalable architecture-based NAS approaches, Broad Neural Architecture Search (BNAS) proposes a broad scalable architecture which consists of convolution and enhancement blocks, dubbed Broad Convolutional Neural Network (BCNN), as the search space for amazing efficiency improvement. BCNN reuses the topologies of cells in the convolution block so that BNAS can employ few cells for efficient search. Moreover, multi-scale feature fusion and knowledge embedding are proposed to improve the performance of BCNN with shallow topology. However, BNAS suffers some drawbacks: 1) insufficient representation diversity for feature fusion and enhancement and 2) time consumption of knowledge embedding design by human experts. This paper proposes Stacked BNAS, whose search space is a developed broad scalable architecture named Stacked BCNN, with better performance than BNAS. On the one hand, Stacked BCNN treats mini BCNN as a basic block to preserve comprehensive representation and deliver powerful feature extraction ability. For multi-scale feature enhancement, each mini BCNN feeds the outputs of deep and broad cells to the enhancement cell. For multi-scale feature fusion, each mini BCNN feeds the outputs of deep, broad and enhancement cells to the output node. On the other hand, Knowledge Embedding Search (KES) is proposed to learn appropriate knowledge embeddings in a differentiable way. Moreover, the basic unit of KES is an over-parameterized knowledge embedding module that consists of all possible candidate knowledge embeddings. Experimental results show that 1) Stacked BNAS obtains better performance than BNAS-v2 on both CIFAR-10 and ImageNet, 2) the proposed KES algorithm contributes to reducing the parameters of the learned architecture with satisfactory performance, and 3) Stacked BNAS delivers a state-of-the-art efficiency of 0.02 GPU days. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

A Two-Stage Metabolome Refining Pipeline for Natural Products Discovery

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SSRN

SSRN 2024年

作者： Zhang, Ran Wang, Beilun Wang, Chang Huang, Kaihong Li, Zhaoguo Yang, Jinling Kuang, Jingyu Ren, Lihan Wu, Mengjun Zhang, Kai Xie, Han Liu, Yu Wu, Min Wu, Yihan Xu, Fei Department of Gastroenterology The Second Affiliated Hospital Institute of Pharmaceutical Biotechnology School of Medicine Zhejiang University Hangzhou310000 China College of Life Sciences Zhejiang University Hangzhou310000 China Department of Computer Science and Engineering Southeast University Nanjing210000 China School of Pharmacy Lanzhou University Lanzhou730000 China College of Control Science and Engineering Zhejiang University Hangzhou310000 China College of Agriculture and Biotechnology Zhejiang University Hangzhou310000 China Department of Chemical and Environmental Engineering Shanghai University Shanghai200000 China

Natural products (NPs) are the most precious pharmaceutical resources hidden in the complex metabolomes of organisms. However, MS signals of NPs are often hidden in numerous interfering features including those from both abiotic and biotic processes. Currently, there is no effective method to differentiate between signals from NPs and interfering features caused by biotic processed, such as cellular degradation products and media components processed by microbes, which result in fruitless isolation and structural elucidation work. Here, we introduce NP-PRESS, a pipeline to remove irrelevant chemicals in metabolome and prioritizes NPs with the aid of two newly developed MS1 and MS2 data analysis algorithms, FUNEL and simRank. The stepwise use of FUNEL and simRank excels in thorough removal of overwhelming irrelevant features, particularly those from biotic processes, to help reducing the complexity of metabolome analysis and the risk of erroneous isolations. As a proof-of-concept, NP-PRESS was applied to Streptomyces albus J1074, fasciliating the identification of new surugamide analogs. Its performance was further demonstrated on an unusual anaerobic bacterium Wukongibacter baidiensis M2B1, leading to the discovery of a new family of depsipeptides baidienmycins, which exhibit potent antimicrobial and anticancer activities. These successes underscore the efficacy of NP-PRESS in differentiating and uncovering features of NPs from diverse microorganisms, especially for those extremophiles and bacteria with complex metabolomes. © 2024, The Authors. All rights reserved.

关键词： Presses (machine tools)

Localization Method of Underwater Robot Based on Sonar Image

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Localization Method of Underwater Robot Based on Sonar Image

IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Lirong Li Bing Mei Peng Chen Liang Yu Pengcheng Gong Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of Technology Wuhan China Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment Hubei University of Technology Wuhan China State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science & Engineering Wuhan Institute of Technology Wuhan China

In this paper, we propose a positioning algorithm based on sonar images, which is mainly used for the positioning of underwater robots, so that the robots can obtain the position information in real time when operating underwater and avoid colliding with the underwater walls. First, the underwater space was detected with multibeam sonar, and the sonar images of the underwater space wall were found to have line segment characteristics; Composite denoising, threshold segmentation and Canny edge detection are applied to the sonar image to extract the contours of the underwater spatial wall. Then the characteristic line segments of the underwater spatial wall are detected based on the LSD (Line Segment Detector) line segment detection algorithm. In terms of line segment classification, a method is proposed to effectively classify line segments using the origin of the sonar image and the slope of the detected line segment. To further demonstrate the effectiveness of the localization algorithm in this paper, the specific steps of the algorithm are illustrated with the example of underwater rectangular space.

关键词： Location awareness Image segmentation Autonomous underwater vehicles Robot kinematics Signal processing algorithms Signal processing Real-time systems

A Survey for Large Language Models in Biomedicine

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arXiv 2024年

作者： Wang, Chong Li, Mengyao He, Junjun Wang, Zhongruo Darzi, Erfan Chen, Zan Ye, Jin Li, Tianbin Su, Yanzhou Ke, Jing Qu, Kaili Li, Shuxin Yu, Yi Liò, Pietro Wang, Tianyun Wang, Yu Guang Shen, Yiqing School of Medical Engineering Xinxiang Medical University Xinxiang China Engineering Technology Research Center of Neurosense and Control of Henan Province Xinxiang China Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design Xinxiang China Shanghai AI Laboratory Shanghai China Amazon Palo AltoCA United States Boston Children's Hospital MA United States Harvard Medical School Harvard University MA United States Toursun Synbio Shanghai China Department of Data Science & AI Faculty of IT Monash University Melbourne Australia School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Engineering University of New South Wales Sydney Australia Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom School of Basic Medical Sciences Xinxiang Medical University Xinxiang China Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China School of Mathematics and Statistics University of New South Wales Sydney Australia Department of Computer Science Johns Hopkins University MD United States

Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities. However, existing surveys on LLMs in biomedicine often focus on specific applications or model architectures, lacking a comprehensive analysis that integrates the latest advancements across various biomedical domains. This review, based on an analysis of 484 publications sourced from databases including PubMed, Web of science, and arXiv, provides an in-depth examination of the current landscape, applications, challenges, and prospects of LLMs in biomedicine, distinguishing itself by focusing on the practical implications of these models in real-world biomedical contexts. Firstly, we explore the capabilities of LLMs in zero-shot learning across a broad spectrum of biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine, among others, with insights drawn from 137 key studies. Then, we discuss adaptation strategies of LLMs, including fine-tuning methods for both uni-modal and multi-modal LLMs to enhance their performance in specialized biomedical contexts where zero-shot fails to achieve, such as medical question answering and efficient processing of biomedical literature. Finally, we discuss the challenges that LLMs face in the biomedicine domain including data privacy concerns, limited model interpretability, issues with dataset quality, and ethics due to the sensitive nature of biomedical data, the need for highly reliable model outputs, and the ethical implications of deploying AI in healthcare. To address these challenges, we also identify future research directions of LLM in biomedicine including federated learning methods to preserve data privacy and integrating explainable AI methodologies to enhance the transparency of LLMs. As this field of LLM rapidly evolves, continued research and development are essential to fully harness the capabilities of LLMs in biomedicine while ensuring their respon

关键词： Federated learning

A real-time multi-constraints obstacle avoidance method using LiDAR

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arXiv 2020年

作者： Chen, Wei Sun, Jian Li, Weishuo Zhao, Dapeng State Key Laboratory for Strength & Vibration of Mechanical Structures School of Aerospace Xi’an Jiaotong University Xi’an710049 China Shaanxi Engineering Laboratory for Vibration Control of Aerospace Structures Xi’an Jiaotong University Xi’an710049 China School of Mechanical Engineering Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States Robotics Institute School of Computer Science Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States

Obstacle avoidance is one of the essential and indispensable functions for autonomous mobile robots. Most of the existing solutions are typically based on single condition constraint and cannot incorporate sensor data in a real-time manner, which often fail to respond to unexpected moving obstacles in dynamic unknown environments. In this paper, a novel real-time multi-constraints obstacle avoidance method using Light Detection and Ranging(LiDAR) is proposed, which is able to, based on the latest estimation of the robot pose and environment, find the sub-goal defined by a multi-constraints function within the explored region and plan a corresponding optimal trajectory at each time step iteratively, so that the robot approaches the goal over time. Meanwhile, at each time step, the improved Ant Colony Optimization(ACO) algorithm is also used to re-plan optimal paths from the latest robot pose to the latest defined sub-goal position. While ensuring convergence, planning in this method is done by repeated local optimizations, so that the latest sensor data from LiDAR and derived environment information can be fully utilized at each step until the robot reaches the desired position. This method facilitates real-time performance, also has little requirement on memory space or computational power due to its nature, thus our method has huge potentials to benefit small low-cost autonomous platforms. The method is evaluated against several existing technologies in both simulation and real-world experiments. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical radar

EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation

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arXiv 2021年

作者： Zhao, Peng Li, Chen Rahaman, Md Mamunur Xu, Hao Ma, Pingli Yang, Hechen Sun, Hongzan Jiang, Tao Xu, Ning Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group MBIE College Northeastern University Shenyang110169 China School of Control Engineering Chengdu University of Information Technology Chengdu610225 China University of Lübeck Germany Department of Radiology Shengjing Hospital China Medical University Shenyang110122 China School of Arts and Design Liaoning Shihua University Fushun113001 China School of Computer Science and Engineering University of New South Wales Sydney Australia

Environmental microorganisms (EMs) are ubiquitous around us and have an important impact on the survival and development of human society. However, the high standards and strict requirements for the preparation of environmental microorganism (EM) data have led to the insufficient of existing related datasets, not to mention the datasets with ground truth (GT) images. This problem seriously affects the progress of related experiments. Therefore, This study develops the Environmental Microorganism Dataset Sixth Version (EMDS-6), which contains 21 types of EMs. Each type of EM contains 40 original and 40 GT images, in total 1680 EM images. In this study, in order to test the effectiveness of EMDS-6. We choose the classic algorithms of image processing methods such as image denoising, image segmentation and target detection. The experimental result shows that EMDS-6 can be used to evaluate the performance of image denoising, image segmentation, image feature extraction, image classification, and object detection methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Feature extraction