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arXiv 2020年

作者： Shi, Feng Wang, Jun Shi, Jun Wu, Ziyan Wang, Qian Tang, Zhenyu He, Kelei Shi, Yinghuan Shen, Dinggang Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai200232 Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai200444 United Imaging Intelligence CambridgeMA02140 United States Institute for Medical Imaging Technology School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200030 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing210093 China National Key Laboratory for Novel Software and Technology Nanjing University Nanjing China

The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI ca n improve work efficiency by accurate delination of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Correction to‘Landscape dynamic network biomarker analysis reveals the tipping point of transcriptome reprogramming to prevent skin photodamage’

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Journal of Molecular Cell Biology 2022年第3期14卷 65-65页

作者： Chengming Zhang Hong Zhang Jing Ge Tingyan Mi Xiao Cui Fengjuan Tu Xuelan Gu Tao Zeng Luonan Chen State Key Laboratory of Cell Biology Shanghai Institute of Biochemistry and Cell BiologyCenter for Excellence in Molecular Cell ScienceChinese Academy of SciencesShanghai 200031China University of Chinese Academy of Sciences Chinese Academy of SciencesBeĳing 100049China Unilever Research&Development Centre Shanghai Shanghai 200335China Bio-Med Big Data Center Shanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China School of Life Science and Technology ShanghaiTech UniversityShanghai 201210China Key Laboratory of Systems Health Science of Zhejiang Province Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesChinese Academy of SciencesHangzhou 310024China Guangdong Institute of Intelligence Science and Technology Zhuhai 519031China

In the originally published version of this article(p.822),there was an error in the second affiliation of author Chengming *** erroneous affiliation was given as‘University of the Chinese Academy of Sciences’.This ... 详细信息

关键词： programming damage network

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Unleashing the Strengths of Unlabeled data in Pan-cancer Abdominal Organ Quantification: The FLARE22 Challenge

arXiv

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arXiv 2023年

作者： Ma, Jun Zhang, Yao Gu, Song Ge, Cheng Ma, Shihao Young, Adamo Zhu, Cheng Meng, Kangkang Yang, Xin Huang, Ziyan Zhang, Fan Liu, Wentao Pan, YuanKe Huang, Shoujin Wang, Jiacheng Sun, Mingze Xu, Weixin Jia, Dengqiang Choi, Jae Won Alves, Natália de Wilde, Bram Koehler, Gregor Wu, Yajun Wiesenfarth, Manuel Zhu, Qiongjie Dong, Guoqiang He, Jian Wang, Bo He, Junjun Yang, Hua Yang, Huihua Huang, Bingding Lyu, Mengye Ma, Yongkang Guo, Heng Zhang, Rongguo Maier-Hein, Klaus The Department of Laboratory Medicine and Pathobiology University of Toronto Peter Munk Cardiac Centre University Health Network Vector Institute Toronto Canada Shanghai AI Laboratory Shanghai200232 China The Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing211113 China Ocean University of China Qingdao266100 China The Department of Computer Science University of Toronto Peter Munk Cardiac Centre University Health Network Vector Institute Toronto Canada Tinavi Medical Technologies Co. Ltd. Beijing100192 China University of Science and Technology Beijing Beijing100083 China The School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518055 China The Department of Radiological Algorithm Fosun Aitrox Information Technology Co. Ltd. Shanghai200033 China The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Shenzhen Technology University Shenzhen518000 China Xiamen University Xiamen361005 China Alibaba Beijing100084 China Infervision Medical Technology Co. Ltd. Beijing100025 China Hong Kong Centre for Cerebro-cardiovascular Health Engineering 000000 Hong Kong The Department of Radiology Armed Forces Yangju Hospital Yangju11429 Korea Republic of The Department of Radiology Radboudumc Nijmegen6525XZ Netherlands The Department of Medical Image Computing German Cancer Research Center Heidelberg69120 Germany ShenZhen Yorktal DMIT LLC Shenzhen518100 China Heidelberg69120 Germany The Department of Nuclear Medicine Nanjing Drum Tower Hospital Nanjing210008 China The College of Big Data and Internet Shenzhen Technology University Shenzhen518188 China The College of Health Science and Environmental Engineering Shenzhen Technology University Shenzhen China The Manteia Technologies Co. Ltd Xiamen China The Alibaba DAMO Academy Beijing China The Peter Munk Cardiac Centre University Health Network Department of Laboratory M

Quantitative organ assessment is an essential step in automated abdominal disease diagnosis and treatment planning. Artificial intelligence (AI) has shown great potential to automatize this process. However, most existing AI algorithms rely on many expert annotations and lack a comprehensive evaluation of accuracy and efficiency in real-world multinational settings. To overcome these limitations, we organized the FLARE 2022 Challenge, the largest abdominal organ analysis challenge to date, to benchmark fast, low-resource, accurate, annotation-efficient, and generalized AI algorithms. We constructed an intercontinental and multinational dataset from more than 50 medical groups, including Computed Tomography (CT) scans with different races, diseases, phases, and manufacturers. We independently validated that a set of AI algorithms achieved a median Dice Similarity Coefficient (DSC) of 90.0% by using 50 labeled scans and 2000 unlabeled scans, which can significantly reduce annotation requirements. The best-performing algorithms successfully generalized to holdout external validation sets, achieving a median DSC of 89.5%, 90.9%, and 88.3% on North American, European, and Asian cohorts, respectively. They also enabled automatic extraction of key organ biology features, which was labor-intensive with traditional manual measurements. This opens the potential to use unlabeled data to boost performance and alleviate annotation shortages for modern AI models. © 2023, CC BY-NC-ND.

关键词： Diseases

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Localization of Deep Inpainting Using High-Pass Fully Convolutional Network

Localization of Deep Inpainting Using High-Pass Fully Convol...

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International Conference on Computer Vision (ICCV)

作者： Haodong Li Jiwu Huang Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Image inpainting has been substantially improved with deep learning in the past years. Deep inpainting can fill image regions with plausible contents, which are not visually apparent. Although inpainting is originally designed to repair images, it can even be used for malicious manipulations, e.g., removal of specific objects. Therefore, it is necessary to identify the presence of inpainting in an image. This paper presents a method to locate the regions manipulated by deep inpainting. The proposed method employs a fully convolutional network that is based on high-pass filtered image residuals. Firstly, we analyze and observe that the inpainted regions are more distinguishable from the untouched ones in the residual domain. Hence, a high-pass pre-filtering module is designed to get image residuals for enhancing inpainting traces. Then, a feature extraction module, which learns discriminative features from image residuals, is built with four concatenated ResNet blocks. The learned feature maps are finally enlarged by an up-sampling module, so that a pixel-wise inpainting localization map is obtained. The whole network is trained end-to-end with a loss addressing the class imbalance. Extensive experimental results evaluated on both synthetic and realistic images subjected to deep inpainting have shown the effectiveness of the proposed method.

关键词： Forensics Feature extraction Convolution Kernel Machine learning Computer vision Probability

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On efficient and robust metrics for RANSAC hypotheses and 3D rigid registration

arXiv

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arXiv 2020年

作者： Yang, Jiaqi Huang, Zhiqiang Quan, Siwen Zhang, Qian Zhang, Yanning Cao, Zhiguo National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University China School of Software Northwestern Polytechnical University Xi’an710129 China School of Electronic and Control Engineering Chang’an University Xi’an710064 China School of Resources and Environment Hubei University Wuhan430062 China National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

This paper focuses on developing efficient and robust evaluation metrics for RANSAC hypotheses to achieve accurate 3D rigid registration. Estimating six-degree-of-freedom (6-DoF) pose from feature correspondences remains a popular approach to 3D rigid registration, where random sample consensus (RANSAC) is a de-facto choice to this problem. However, existing metrics for RANSAC hypotheses are either time-consuming or sensitive to common nuisances, parameter variations, and different application scenarios, resulting in performance deterioration in overall registration accuracy and speed. We alleviate this problem by first analyzing the contributions of inliers and outliers, and then proposing several efficient and robust metrics with different designing motivations for RANSAC hypotheses. Comparative experiments on four standard datasets with different nuisances and application scenarios verify that the proposed metrics can significantly improve the registration performance and are more robust than several state-of-the-art competitors, making them good gifts to practical applications. This work also draws an interesting conclusion, i.e., not all inliers are equal while all outliers should be equal, which may shed new light on this research problem. Copyright © 2020, The Authors. All rights reserved.

关键词： Deterioration

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Substrate-independent nano-coating with persistent antibacterial and tooth whitening activities for dental health

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Chinese Chemical Letters 2025年

作者： Yi Liu Xiaolin Yu Wenyun Mu Minsi Meng Baixue Li Jie Liu Haixin Qian Lin Weng Tingting Yu Nan Hu Xin Chen Yi Hao College of Chemical Engineering Sichuan University of Science & Engineering Zigong 643000 China Institute of Precision Medicine Zigong Academy of Big Data and Artificial Intelligence in Medical Science Zigong Fourth People's Hospital Zigong 643000 China Department of Chemical Engineering Shaanxi Key Laboratory of Energy Chemical Process Intensification Institution of Polymer Science in Chemical Engineering School of Chemical Engineering and Technology Xi'an Jiaotong University Xi'an 710049 China Sichuan Clinical Research Center for Clinical Laboratory Zigong Fourth People's Hospital Zigong 643000 China Department of Laboratory Medicine Zigong Fourth People's Hospital Zigong 643000 China Department of Prosthodontics Shanghai Ninth People's Hospital College of Stomatology Shanghai Jiao Tong University School of Medicine National Clinical Research Center of Stomatology Shanghai Key Laboratory of Stomatology and Shanghai Research Institute of Stomatology Shanghai 200240 China

Bacteria and stains on tooth and various dental materials severely harm dental health and beauty and require feasible solutions. In this study, a simple strategy was developed to produce nano-coating on different substrates for persistent antibacterial and whitening. The coating is formed by the lysozyme (Lys), hemoglobin (Hb), and glucose oxidase (GOD) via co-assembly, in which the phase transition of Lys initiated the co-assembly to anchor other two proteins. During therapy, the GOD continuously oxidizes glucose in the oral environment to cut off the nutrition of bacteria meanwhile generating H 2 O 2 , which would be further catalyzed by the ferrous ions in Hb to produce reactive oxygen species (ROS) for effective decomposition of surrounding bacteria and stains. Moreover, the Hb can perform persistent release of oxygen, which not only enhances the efficiency of glucose oxidation to produce more ROS but directly suppresses anaerobic bacteria via reversing the local hypoxia environment in the mouth. The experimental results indicated that our strategy is able to form nano-film of proteins both on the surface of dental orthosis and human tooth, which further causes obvious reduction of the bacteria not only on the coated substrate but in the surrounding tissue with up to 100% of the bacteriostatic rate. In addition, both the dental orthosis and human tooth were also rapidly cleaned due to the local ROS generation, leading to a sustained anti-staining property in the long term.

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Reduced Cortical Surface Area in the Frontal Operculum as a Causal Risk Predictor for Chronic Pain

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Pain Research and Management 2025年第1期2025卷

作者： Xiuzhi Wang Yipeng Le Xichen Wang Yingchao Song Qian Su Xiaoxiao Xiao Yifan Li Wen Qin Chunshui Yu Meng Liang School of Medical Technology School of Medical Imaging Tianjin Key Laboratory of Functional Imaging The Province and Ministry Cosponsored Collaborative Innovation Center for Medical Epigenetics Tianjin Medical University Tianjin 300070 China *** College of Artificial Intelligence and Big Data for Medical Sciences Shandong First Medical University & Shandong Academy of Medical Sciences Shandong 250117 China *** Department of Molecular Imaging and Nuclear Medicine Tianjin Medical University Cancer Institute and Hospital National Clinical Research Center for Cancer Tianjin Key Laboratory of Cancer Prevention and Therapy Tianjin’s Clinical Research Center for China Tianjin 300060 China *** Department of Radiology State Key Laboratory of Experimental Hematology Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology Tianjin Medical University General Hospital Tianjin 300052 China ***

Chronic pain is a prevalent and debilitating condition that imposes substantial personal and societal burdens. Despite its significance, the neural mechanisms underlying individual susceptibility to chronic pain remain inadequately understood. In this study, we examined the prospective associations between 1325 brain structural imaging phenotypes and the future risk of developing chronic pain in a UK Biobank cohort of 5754–5756 participants. These phenotypes encompassed regional and tissue volume, cortical surface area and thickness. General linear models (GLMs) were employed to identify brain structural variations associated with the risk of developing chronic pain, and then Mendelian randomization (MR) was employed to explore potential causal relationships between brain structure and chronic pain development. GLMs identified three significant associations between imaging phenotypes and the future development of chronic pain. All three imaging phenotypes pertained to the cortical surface area of the frontal operculum, albeit derived from three different brain atlases. Specifically, reduced cortical surface area in the frontal operculum was significantly associated with an increased risk of developing chronic pain: BA atlas area 44 ( T = −4.10, p = 4.24 × 10 −5 ), Desikan atlas pars opercularis ( T = −4.21, p = 2.55 × 10 −5 ), and DKT atlas pars opercularis ( T = −3.96, p = 7.47 × 10 −5 ). Subsequent MR analysis further demonstrated a causally protective effect of larger cortical area in the prefrontal operculum against the risk of developing chronic pain (OR = 0.91, p = 1.91 × 10 −2 ). These results indicate a critical role of the surface area of frontal operculum in individual chronic pain susceptibility and provide a potential risk predictor for chronic pain development.

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eGPS 1.0: comprehensive software for multi-omic and evolutionary analyses

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National Science Review 2019年第5期6卷 867-869页

作者： Dalang Yu Lili Dong Fangqi Yan Hailong Mu Bixia Tang Xiao Yang Tao Zeng Qing Zhou Feng Gao Zhonghuang Wang Ziqian Hao Hongen Kang Yi Zheng Hongwei Huang Yuzhang Wei Wei Pan Yaochen Xu Junwei Zhu Shilei Zhao Ciran Wang Pengyu Wang Long Dai Mushan Li Li Lan Yiwei Wang Hua Chen Yi-Xue Li Yun-Xin Fu Zhen Shao Yiming Bao Fangqing Zhao Luo-Nan Chen Guo-Qing Zhang Wenming Zhao Haipeng Li CAS Key Laboratory of Computational Biology CAS-MPG Partner Institute for Computational Biology Shanghai Institute of Nutrition and HealthShanghai Institutes for Biological SciencesUniversity of Chinese Academy of Sciences Chinese Academy of Sciences University of Chinese Academy of Sciences BIG Data Center Beijing Institute of GenomicsChinese Academy of Sciences Shanghai Center for Bioinformation Technology Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences CAS Laboratory of Genomic and Precision Medicine Beijing Institute of Genomics Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Department of Biostatistics and Data Science School of Public Health University of Texas Health Science Center at Houston Computational Genomics Lab Beijing Institutes of Life Science Chinese Academy of Sciences Shanghai Research Center for Brain Science and Brain-Inspired Intelligence

It has become increasingly challenging for researchers to analyze the exponentially expanding amount of multi-omic ***, we describe multi-functional software named evolutionary GenotypePhenotype Systems (eGPS) that en... 详细信息

关键词： GPS eGPS 1.0 comprehensive software for multi-omic and evolutionary analyses

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Erratum to "Multi-View Face Synthesis via Progressive Face Flow"

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2021年 30卷 6700页

作者： Yangyang Xu Xuemiao Xu Jianbo Jiao Keke Li Cheng Xu Shengfeng He School of Computer Science and Engineering South China University of Technology Guangzhou China State Key Laboratory of Subtropical Building Science the Key Laboratory of Big Data and Intelligent Robot Ministry of Education and the Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information South China University of Technology Guangzhou China University of Oxford Oxford U.K.

In the above article [1], unfortunately, Fig. 5 was not displayed correctly with many empty images. The correct version is supplemented here.

关键词： Faces

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KE-GAN: Knowledge Embedded Generative Adversarial Networks for Semi-Supervised Scene Parsing

KE-GAN: Knowledge Embedded Generative Adversarial Networks f...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Mengshi Qi Yunhong Wang Jie Qin Annan Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Inception Institute of Artificial Intelligence UAE

In recent years, scene parsing has captured increasing attention in computer vision. Previous works have demonstrated promising performance in this task. However, they mainly utilize holistic features, whilst neglecting the rich semantic knowledge and inter-object relationships in the scene. In addition, these methods usually require a large number of pixel-level annotations, which is too expensive in practice. In this paper, we propose a novel Knowledge Embedded Generative Adversarial Networks, dubbed as KE-GAN, to tackle the challenging problem in a semi-supervised fashion. KE-GAN captures semantic consistencies of different categories by devising a Knowledge Graph from the large-scale text corpus. In addition to readily-available unlabeled data, we generate synthetic images to unveil rich structural information underlying the images. Moreover, a pyramid architecture is incorporated into the discriminator to acquire multi-scale contextual information for better parsing results. Extensive experimental results on four standard benchmarks demonstrate that KE-GAN is capable of improving semantic consistencies and learning better representations for scene parsing, resulting in the state-of-the-art performance.

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