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The vitruvian baby: Interactive reformation of fetal ultrasound data to a T-position

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The vitruvian baby: Interactive reformation of fetal ultraso...

2019 Eurographics Workshop on visual computing for Biology and Medicine, VCBM 2019

作者： Mörth, Eric Raidou, Renata G. Viola, Ivan Smit, Noeska N. Department of Informatics University of Bergen Norway Institute of Visual Computing and Human-Centered Technology Vienna University of Technology Austria Mohn Medical Imaging and Visualization Centre Haukeland University Hospital Norway Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Saudi Arabia

ISBN: (纸本)9783038680819

Three-dimensional (3D) ultrasound imaging and visualization is often used in medical diagnostics, especially in prenatal screening. Screening the development of the fetus is important to assess possible complications early on. State of the art approaches involve taking standardized measurements to compare them with standardized tables. The measurements are taken in a 2D slice view, where precise measurements can be difficult to acquire due to the fetal pose. Performing the analysis in a 3D view would enable the viewer to better discriminate between artefacts and representative information. Additionally making data comparable between different investigations and patients is a goal in medical imaging techniques and is often achieved by standardization. With this paper, we introduce a novel approach to provide a standardization method for 3D ultrasound fetus screenings. Our approach is called "The Vitruvian Baby" and incorporates a complete pipeline for standardized measuring in fetal 3D ultrasound. The input of the method is a 3D ultrasound screening of a fetus and the output is the fetus in a standardized T-pose. In this pose, taking measurements is easier and comparison of different fetuses is possible. In addition to the transformation of the 3D ultrasound data, we create an abstract representation of the fetus based on accurate measurements. We demonstrate the accuracy of our approach on simulated data where the ground truth is known. © 2019 The Author(s) Eurographics Proceedings © 2019 The Eurographics Association.

关键词： Standardization

Interference-assisted kaleidoscopic meta-plexer for arbitrary spin-wavefront manipulation

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Light(Science & Applications) 2019年第1期8卷 1141-1150页

作者： He-Xiu Xu Guangwei Hu Ying Li Lei Han Jianlin Zhao Yunming Sun Fang Yuan Guang-Ming Wang Zhi Hao Jiang Xiaohui Ling Tie Jun Cui Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Air and Missile Defense College Air force Engineering University710051 Xi’anChina Hunan Provincial Key Laboratory of Intelligent Information Processing and Applications College of Physics and Electronics EngineeringHengyang Normal University421008 HengyangChina School of Natural and Applied Sciences Northwestern Polytechnical University710072 Xi’anChina Advanced Technique Department Key Lab of Aeronautics Computing Technique710075 Xi’anChina State Key Laboratory of Millimeter Waves Southeast University210096 NanjingChina

Achieving simultaneous polarization and wavefront control,especially circular polarization with the auxiliary degree of freedom of light and spin angular momentum,is of fundamental importance in many optical *** are typically undesirable in highly integrated photonic circuits and ***,we propose an interference-assisted metasurface-multiplexer(meta-plexer)that counterintuitively exploits constructive and destructive interferences between hybrid meta-atoms and realizes independent spin-selective wavefront *** kaleidoscopic meta-plexers are experimentally demonstrated via two types of single-layer spinwavefront multiplexers that are composed of spatially rotated anisotropic *** type generates a spinselective Bessel-beam wavefront for spin-down light and a low scattering cross-section for stealth for spin-up *** other type demonstrates versatile control of the vortex wavefront,which is also characterized by the orbital angular momentum of light,with frequency-switchable numbers of beams under linearly polarized wave *** findings offer a distinct interference-assisted concept for realizing advanced multifunctional photonics with arbitrary and independent spin-wavefront features.A variety of applications can be readily anticipated in optical diodes,isolators,and spin-Hall meta-devices without cascading bulky optical elements.

关键词： arbitrary momentum versatile

用于糖尿病培训的大型语言模型:一项前瞻性研究（英文）

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Science Bulletin 2025年第6期 934-942页

作者：李灏萱江泽铧管洲榆包玉倩刘月星胡婷婷李佳佳刘茹涵吴量程棣纪宏伟王勇王亚星 Carol Y.Cheung 郑颖丰王继红李震吴卫兵 Cynthia Ciwei Lim Yong Mong Bee Hong Chang Tan Elif I.Ekinci David C.Klonoff Justin B.Echouffo-Tcheugui Nestoras Mathioudakis Leonor Corsino Rafael Simó Charumathi Sabanayagam Gavin Siew Wei Tan 程景煜黄天荫李华婷蔡淳毛丽娟 Lee-Ling Lim 覃宇宗盛斌贾伟平 Shanghai University of Sport School of Clinical Medicine Beijing Tsinghua Changgung Hospital Tsinghua Medicine Tsinghua University Beijing Visual Science and Translational Eye Research Institute(BERI) Tsinghua Medicine Tsinghua University Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Department of Cardiology the Affiliated Hospital of Qingdao University CEMS NCMIS HCMS MADIS Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematics University of Chinese Academy of Sciences Chinese Academy of Sciences Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Beijing Institute of Ophthalmology Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular Diseases Department of Renal Medicine Singapore General Hospital Sing Health-Duke Academic Medical Centre Department of Endocrinology Singapore General Hospital Department of Endocrinology Austin Health Department of Medicine The University of Melbourne(Austin Health) Australian Centre for Accelerating Diabetes Innovations(ACADI) The Univ

Diabetes poses a considerable global health challenge, with varying levels of diabetes knowledge among healthcare professionals, highlighting the importance of diabetes training. Large Language Models(LLMs)provide new insights into diabetes training, but their performance in diabetes-related queries remains uncertain, especially outside the English language like Chinese. We first evaluated the performance of ten LLMs: Chat GPT-3.5, Chat GPT-4.0, Google Bard, Lla MA-7B, Lla MA2-7B, Baidu ERNIE Bot, Ali Tongyi Qianwen, Med GPT, Huatuo GPT, and Chinese Lla MA2-7B on diabetes-related queries, based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC) and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United Kingdom. Second, we assessed the training of primary care physicians(PCPs) without and with the assistance of Chat GPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical assistants. We found that Chat GPT-4.0 outperformed other LLMs in the English examination, achieving a passing accuracy of 62.50%, which was significantly higher than that of Google Bard, Lla MA-7B, and Lla MA2-7B. For the NCE-CPFC examination, Chat GPT-4.0, Ali Tongyi Qianwen, Baidu ERNIE Bot, Google Bard, Med GPT, and Chat GPT-3.5 successfully passed, whereas Lla MA2-7B, Huatuo GPT,Chinese LLa MA2-7B, and Lla MA-7B failed. Chat GPT-4.0(84.82%) surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%). In summary, LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language, and hold great potential to assist future diabetes training for physicians globally.

关键词：

Performance modeling and analysis of a hyperledger-based system using gspn

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arXiv 2020年

作者： Yuan, Pu Zheng, Kan Xiong, Xiong Zhang, Kuan Lei, Lei Intelligent Computing and Communications Lab Key Lab of Universal Wireless Communications Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering University of NebraskaLincoln OmahaNE68182 United States College of Science and Engineering James Cook University DouglasQLD4814 Australia

As a highly scalable permissioned blockchain platform, Hyperledger Fabric supports a wide range of industry use cases ranging from governance to finance. In this paper, we propose a model to analyze the performance of a Hyperledger-based system by using Generalised Stochastic Petri Nets (GSPN). This model decomposes a transaction flow into multiple phases and provides a simulation-based approach to obtain the system latency and throughput with a specific arrival rate. Based on this model, we analyze the impact of different configurations of ordering service on system performance to find out the bottleneck. Moreover, a mathematical configuration selection approach is proposed to determine the best configuration which can maximize the system throughput. Finally, extensive experiments are performed on a running system to validate the proposed model and approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Petri nets

Lightweight Image Super-Resolution with Enhanced CNN

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arXiv 2020年

作者： Tian, Chunwei Zhuge, Ruibin Wu, Zhihao Xu, Yong Zuo, Wangmeng Chen, Chen Lin, Chia-Wen Bio-Computing Research Center Harbin Institute of Technology Shenzhen ShenzhenGuangdong518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition ShenzhenGuangdong518055 China Peng Cheng Laboratory ShenzhenGuangdong518055 China School of Computer Science and Technology Harbin Institute of Technology HarbinHeilongjiang150001 China Department of Electrical and Computer Engineering University of North Carolina at Charlotte NC28223 United States Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan

Deep convolutional neural networks (CNNs) with strong expressive ability have achieved impressive performances on single image super-resolution (SISR). However, their excessive amounts of convolutions and parameters usually consume high computational cost and more memory storage for training a SR model, which limits their applications to SR with resource-constrained devices in real world. To resolve these problems, we propose a lightweight enhanced SR CNN (LESRCNN) with three successive sub-blocks, an information extraction and enhancement block (IEEB), a reconstruction block (RB) and an information refinement block (IRB). Specifically, the IEEB extracts hierarchical low-resolution (LR) features and aggregates the obtained features step-by-step to increase the memory ability of the shallow layers on deep layers for SISR. To remove redundant information obtained, a heterogeneous architecture is adopted in the IEEB. After that, the RB converts low-frequency features into high-frequency features by fusing global and local features, which is complementary with the IEEB in tackling the long-term dependency problem. Finally, the IRB uses coarse high-frequency features from the RB to learn more accurate SR features and construct a SR image. The proposed LESRCNN can obtain a high-quality image by a model for different scales. Extensive experiments demonstrate that the proposed LESRCNN outperforms state-of-the-arts on SISR in terms of qualitative and quantitative evaluation. The code of LESRCNN is accessible on https://***/hellloxiaotian/LESRCNN. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

R-Bench: Are your Large Multimodal Model Robust to Real-world Corruptions?

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IEEE Journal on Selected Topics in Signal Processing 2025年

作者： Li, Chunyi Zhang, Jianbo Zhang, Zicheng Wu, Haoning Tian, Yuan Sun, Wei Lu, Guo Min, Xiongkuo Liu, Xiaohong Lin, Weisi Zhang, Xiao-Ping Zhai, Guangtao Shanghai Jiao Tong University Institute of Image Communication and Network Engineering Shanghai200240 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Shanghai AI Lab Shanghai200232 China East China Normal University School of Communication and Electronic Engineering Shanghai200240 China Shanghai Jiao Tong University John Hopcroft Center Shanghai200240 China Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering TorontoONM5B 2K3 Canada

The outstanding performance of Large Multimodal Models (LMMs) has made them widely applied in vision-related tasks. However, various corruptions in the real world mean that images will not be as ideal as in simulations, presenting significant challenges for the practical application of LMMs. To address this issue, we introduce R-Bench, a benchmark focused on the Real-world Robustness of LMMs. Specifically, we: (a) model the complete link from user capture to LMMs reception, comprising 33 corruption dimensions, including 7 steps according to the corruption sequence, and 7 groups based on low-level attributes;(b) collect reference/distorted image dataset before/after corruption, including 2,970 question-answer pairs with human labeling;(c) propose comprehensive evaluation for absolute/relative robustness and benchmark 20 mainstream LMMs. Results show that while LMMs can correctly handle the original reference images, their performance is not stable when faced with distorted images, and there is a significant gap in robustness compared to the human visual system. We hope that R-Bench will inspire improving the robustness of LMMs, extending them from experimental simulations to the real-world application. © 2007-2012 IEEE.

关键词： AI-Generated Content Image Compression Perceptual Quality Ultra-Low Bitrate

CRNN based jersey-bib number/text recognition in sports and marathon images 15

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CRNN based jersey-bib number/text recognition in sports and ...

15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

作者： Nag, Sauradip Ramachandra, Raghavendra Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Kankanhalli, Mohan Department of Computer Science & Engineering Kalyani Government Engineering College Kalyani India Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Norway Faculty of Computer System and Information Technology University of Malaya Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University China Department of Computer Science School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9781728128610

The primary challenge in tracing the participants in sports and marathon video or images is to detect and localize the jersey/Bib number that may present in different regions of their outfit captured in cluttered environment conditions. In this work, we proposed a new framework based on detecting the human body parts such that both Jersey Bib number and text is localized reliably. To achieve this, the proposed method first detects and localize the human in a given image using Single Shot Multibox Detector (SSD). In the next step, different human body parts namely, Torso, Left Thigh, Right Thigh, that generally contain a Bib number or text region is automatically extracted. These detected individual parts are processed individually to detect the Jersey Bib number/text using a deep CNN network based on the 2-channel architecture based on the novel adaptive weighting loss function. Finally, the detected text is cropped out and fed to a CNN-RNN based deep model abbreviated as CRNN for recognizing jersey/Bib/text. Extensive experiments are carried out on the four different datasets including both bench-marking dataset and a new dataset. The performance of the proposed method is compared with the state-of-the-art methods on all four datasets that indicates the improved performance of the proposed method on all four datasets. © 2019 IEEE.

关键词： Sports

Lightweight Image Super-Resolution with Enhanced CNN

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arXiv 2020年

作者： Tian, Chunwei Zhuge, Ruibin Wu, Zhihao Xu, Yong Zuo, Wangmeng Chen, Chen Lin, Chia-Wen Bio-Computing Research Center Harbin Institute of Technology Shenzhen ShenzhenGuangdong518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition ShenzhenGuangdong518055 China Peng Cheng Laboratory ShenzhenGuangdong518055 China School of Computer Science and Technology Harbin Institute of Technology HarbinHeilongjiang150001 China Department of Electrical and Computer Engineering University of North Carolina CharlotteNC28223 United States Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan

关键词： Convolutional neural networks

ADAM CHALLENGE: DETECTING AGE-RELATED MACULAR DEGENERATION FROM FUNDUS IMAGES

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arXiv 2022年

作者： Fang, Huihui Li, Fei Fu, Huazhu Sun, Xu Cao, Xingxing Lin, Fengbin Son, Jaemin Kim, Sunho Quellec, Gwenole Matta, Sarah Shankaranarayana, Sharath M. Chen, Yi-Ting Wang, Chuen-Heng Shah, Nisarg A. Lee, Chia-Yen Hsu, Chih-Chung Xie, Hai Lei, Baiying Baid, Ujjwal Innani, Shubham Dang, Kang Shi, Wenxiu Kamble, Ravi Singhal, Nitin Wang, Ching-Wei Lo, Shih-Chang Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The ADAM Challenge State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore CONICET Yatiris lab of Pladema Institute Tandil Argentina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Inserm UMR 1101 BrestF-29200 France Univ Bretagne Occidentale BrestF-29200 France Zasti India Pvt. Ltd. India Graphen.Inc Muen Biomedical and Optoelectronics Technologies Inc Taiwan Department of Electrical Engineering Indian Institute of Technology Rajasthan Jodhpur India Department of Electrical Engineering National United University Taiwan Institute of Data Science Department of Statistics National Cheng Kung University Taiwan Shenzhen University Shenzhen China SGGS Institute of Engineering and Technology Nanded India Suzhou Tisu Information Technology Co. Ltd China AIRA Matrix Mumbai India Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taiwan

Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss caused by this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM), which was held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. As part of the ADAM challenge, we have released a comprehensive dataset of 1200 fundus images with AMD diagnostic labels, pixel-wise segmentation masks for both optic disc and AMD-related lesions (drusen, exudates, hemorrhages and scars, among others), as well as the coordinates corresponding to the location of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models using this dataset. During the ADAM challenge, 610 results were submitted for online evaluation, with 11 teams finally participating in the onsite challenge. This paper introduces the challenge, the dataset and the evaluation methods, as well as summarizes the participating methods and analyzes their results for each task. In particular, we observed that the ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models. © 2022, CC BY-NC-ND.

关键词： Cost effectiveness