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A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections

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The Innovation 2024年第4期5卷 63-73,62页

作者： Jun Shao Jiechao Ma yizhou yu Shu Zhang Wenyang Wang Weimin Li Chengdi Wang Department of Pulmonary and Critical Care Medicine State Key Laboratory of Respiratory Health and MultimorbidityWest China HospitalWest China School of MedicineSichuan UniversityChengdu 610041China Targeted Tracer Research and Development Laboratory Institute of Respiratory HealthFrontiers Science Center for Disease-related Molecular NetworkWest China HospitalSichuan UniversityChengdu 610213China Frontiers Medical Center Tianfu Jincheng LaboratoryChengdu 610213China AI Lab Deepwise HealthcareBeijing 100080China Department of Computer Science The University of Hong KongHong Kong SARChina

Pulmonary infections pose formidable challenges in clinical settings with high mortality rates across all age groups *** diagnosis and early intervention are crucial to improve patient *** intelligence(AI)has the capability to mine imaging features specific to different pathogens and fuse multimodal features to reach a synergistic diagnosis,enabling more precise investigation and individualized clinical *** this study,we successfully developed a multimodal integration(MMI)pipeline to differentiate among bacterial,fungal,and viral pneumonia and pulmonary tuberculosis based on a real-world dataset of 24,107 *** area under the curve(AUC)of the MMI system comprising clinical text and computed tomography(CT)image scans yielded 0.910(95%confidence interval[CI]:0.904–0.916)and 0.887(95%CI:0.867–0.909)in the internal and external testing datasets respectively,which were comparable to those of experienced ***,the MMI system was utilized to rapidly differentiate between viral subtypes with a mean AUC of 0.822(95%CI:0.805–0.837)and bacterial subtypes with a mean AUC of 0.803(95%CI:0.775–0.830).Here,the MMI system harbors the potential to guide tailored medication recommendations,thus mitigating the risk of antibiotic ***,the integration of multimodal factors in the AI-driven system also provided an evident advantage in predicting risks of developing critical illness,contributing to more informed clinical *** revolutionize medical care,embracing multimodal AI tools in pulmonary infections will pave the way to further facilitate early intervention and precise management in the foreseeable future.

关键词： diagnosis modal integration

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A robust high-resolution details preserving denoising algorithm for meshes

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Science China(Information Sciences) 2013年第9期56卷 38-49页

作者： FAN HanQi PENG QunSheng yu yizhou College of Information Engineering North China University of Technology State Key Laboratory of CAD&CG Zhejiang University Department of Computer Science The University of HongKong

Finely captured meshes often contain details like sharp edges, corners and shallow features. In order to improve the quality of these meshes, we present a robust and efficient high-resolution details-preserving mesh denoising algorithm. Our method consists of three stages. For each triangular face and its face neighborhood, we improve a robust density-based clustering method and apply it to the face neighborhood to extract a subset of neighbors which belong to the same cluster as the central face. And then, we filter the central face normal iteratively within this subset to remove noise. Because the faces within the extracted subset are not distributed across high-resolution details, our normal filtering can preserve such details as much as possible. Finally, we update the vertex positions to be consistent with the filtered face normals using a least-squares formulation. Experiments on various types of meshes indicate that our method has advantages over previous surface denoising methods.

关键词： mesh denoising high-resolution details face neighborhood normal clustering normal filtering

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WaveNano:a signal-level nanopore base-caller via simultaneous prediction of nucleotide labels and move labels through bi-directional WaveNets

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Frontiers of Electrical and Electronic Engineering in China 2018年第4期6卷 359-368页

作者： Sheng Wang Zhen Li yizhou yu Xin Gao Computational Bioscience Research Center (CBRC) King Abdullah University of Science and Technology (KAUST)Thuwa23955-6900Kingdom of Saudi Arabia Department of Computer Science University of Hong KongHong Kong SAR 999077China School of Science and Engineering Shenzhen Research Institute of Big DataThe Chinese University of Hong Kong Shenzhen 518172China

The Oxford MinION nanopore sequencer is the recently appealing third-generation genome sequencing device that is portable and no larger than a cellphone. Despite the benefits of MinION to sequence ultra-long reads in real-time, the high error rate of the existing base-calling methods, especially indels (insertions and deletions), prevents its use in a variety of applications. In this paper, we show that such indel errors are largely due to the segmentation process on the input electrical current signal from MinION. All existing methods conduct segmentation and nucleotide label prediction in a sequential manner, in which the errors accumulated in the first step will irreversibly influence the final base-calling. We further show that the indel issue can be significantly reduced via accurate labeling of nucleotide and move labels directly from the raw signal, which can then be efficiently learned by a bi-directionalWaveNet model simultaneously through feature sharing. Our bi-directional WaveNet model with residual blocks and skip connections is able to capture the extremely long dependency in the raw signal. Taking the predicted move as the segmentation guidance, we employ the Viterbi decoding to obtain the final base-calling results from the smoothed nucleotide probability matrix. Our proposed base-caller, WaveNano, achieves good performance on real MinION sequencing data from Lambda phage. The signal-level nanopore base-callerWaveNano can obtain higher base-calling accuracy, and generate fewer insertions/deletions in the base-called sequences.

关键词： nanopore sequencing bi-directional WaveNets base-calling third generation sequencing deep learning

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Structural Connectivity Enhanced Anisotropic 3D Network for Brain Midline Delineation

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Journal of Beijing Institute of Technology 2023年第5期32卷 562-578页

作者： yufan Liu Kongming Liang Yinuo Jing Shen Wang Zhanyu Ma Yiming Li yizhou yu yizhou Wang Jun Guo Pattern Recognition and Intelligent System Laboratory School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijing 100876China. Center on Frontiers of Computing Studies School of Computer SciencePeking UniversityBeijing 100871China AI Lab Deepwise HealthcareBeijing 100089China

Brain midline delineation can facilitate the clinical evaluation of brain midline shift,which has a pivotal role in the diagnosis and prognosis of various brain ***,there are still challenges for brain midline delineation:1)the largely deformed midline is hard to localize if mixed with severe cerebral hemorrhage;2)the predicted midlines of recent methods are not smooth and continuous which violates the structural *** overcome these challenges,we propose an anisotropic three dimensional(3D)network with context-aware refinement(A3D-CAR)for brain midline *** proposed network fuses 3D context from different two dimensional(2D)slices through asymmetric context *** exploit the elongated structure of the midline,an anisotropic block is designed to balance the difference between the adjacent pixels in the horizontal and vertical *** maintaining the structural priority of a brain midline,we present a novel 3D connectivity regular loss(3D CRL)to penalize the disconnectivity between nearby *** experiments on the CQ dataset and one in-house dataset show that the proposed method outperforms three state-of-the-art methods on four evaluation metrics without excessive computational burden.

关键词： brain midline delineation refinement network structure prior connectivity regular loss

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The long-term health outcomes, pathophysiological mechanisms and multidisciplinary management of long COVID

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Signal Transduction and Targeted Therapy 2023年第12期8卷 5584-5602页

作者： Jingwei Li yun Zhou Jiechao Ma Qin Zhang Jun Shao Shufan Liang yizhou yu Weimin Li Chengdi Wang Department of Pulmonary and Critical Care Medicine State Key Laboratory of Respiratory Health and MultimorbidityMed-X Center for ManufacturingFrontiers Science Center for Disease-related Molecular NetworkWest China HospitalSichuan UniversityChengduChina AI Lab Deepwise HealthcareBeijingChina Department of Postgraduate Student West China HospitalWest China School of MedicineSichuan UniversityChengduChina Department of Computer Science The University of Hong KongHong KongChina

There have been hundreds of millions of cases of coronavirus disease 2019(COVID-19),which is caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).With the growing population of recovered patients,it is crucial to understand the long-term consequences of the disease and management *** COVID-19 was initially considered an acute respiratory illness,recent evidence suggests that manifestations including but not limited to those of the cardiovascular,respiratory,neuropsychiatric,gastrointestinal,reproductive,and musculoskeletal systems may persist long after the acute *** persistent manifestations,also referred to as long COVID,could impact all patients with COVID-19 across the full spectrum of illness ***,we comprehensively review the current literature on long COVID,highlighting its epidemiological understanding,the impact of vaccinations,organ-specific sequelae,pathophysiological mechanisms,and multidisciplinary management *** addition,the impact of psychological and psychosomatic factors is also *** these crucial findings on long COVID,the current diagnostic and therapeutic strategies based on previous experience and pilot studies remain inadequate,and well-designed clinical trials should be prioritized to validate existing ***,we propose the primary challenges concerning biological knowledge gaps and efficient remedies as well as discuss the corresponding recommendations.

关键词： mechanisms consequences hundreds

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Distribution Atlas of COVID-19 Pneumonia on Computed Tomography:A Deep Learning Based Description

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Phenomics 2021年第2期1卷 62-72页

作者： Shan Huang yuancheng Wang Zhen Zhou Qian yu yizhou yu Yi Yang Shenghong Ju Jiangsu Key Laboratory of Molecular and Functional Imaging Department of RadiologyZhongda HospitalMedical School of Southeast University87 Ding Jia Qiao RoadNanjing 210009JiangsuChina School of Electronics Engineering and Computer Science Peking UniversityBeijingChina Department of Computer Science The University of Hong KongHong KongChina Department of Critical Care Medicine Zhongda HospitalSchool of MedicineSoutheast UniversityNanjingChina

Objectives To construct a distribution atlas of coronavirus disease 2019(COVID-19)pneumonia on computed tomography(CT)and further explore the difference in distribution by location and disease severity through a retrospective study of 484 cases in Jiangsu,*** All patients diagnosed with COVID-19 from January 10 to February 18 in Jiangsu Province,China,were enrolled in our *** patients were further divided into asymptomatic/mild,moderate,and severe/critically ill groups.A deep learning algorithm was applied to the anatomic pulmonary segmentation and pneumonia lesion *** frequency of opacity on CT was calculated,and a color-coded distribution atlas was built.A further comparison was made between the upper and lower lungs,between bilateral lungs,and between various severity *** lesion-based radiomics analysis was performed to ascertain the features associated with the disease *** A total of 484 laboratory-confirmed patients with 945 repeated CT scans were *** opacity was mainly distributed in the subpleural and peripheral *** distances from the opacity to the nearest parietal/visceral pleura were shortest in the asymptomatic/mild *** diffused lesions were found in the severe/critically ill *** frequency of opacity increased with increased severity and peaked at about 3-4 or 7-8 o’clock direction in the upper lungs,as opposed to the 5 or 6 o’clock direction in the lower *** with greater energy,more circle-like,and greater surface area were more likely found in severe/critically ill cases than the *** This study constructed a detailed distribution atlas of COVID-19 pneumonia and compared specific patterns in different parts of the lungs at various *** radiomics features most associated with the severity were also *** results may be valuable in determining the COVID-19 sub-phenotype.

关键词： COVID-19 Computed tomography Deep learning Distribution atlas Radiomics

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STGNN-LMR:A Spatial–Temporal Graph Neural Network Approach Based on sEMG Lower Limb Motion Recognition

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Journal of Bionic Engineering 2024年第1期21卷 256-269页

作者： Weifan Mao Bin Ma Zhao Li Jianxing Zhang yizhou Lu Zhuting yu Feng Zhang Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210China University of Chinese Academy of Sciences Beijing 101408China University of Science and Technology of China Hefei 230026China Shanghai Zhongyan Jiuyi Technology Co. Ltd.Shanghai 201821China Academy of Military Science Bejing 100091China

Lower limb motion recognition techniques commonly employ Surface Electromyographic Signal(sEMG)as input and apply a machine learning classifier or Back Propagation Neural Network(BPNN)for ***,this artificial feature engineering technique is not generalizable to similar tasks and is heavily reliant on the researcher’s subject *** contrast,neural networks such as Convolutional Neural Network(CNN)and Long Short-term Memory Neural Network(LSTM)can automatically extract features,providing a more generalized and adaptable approach to lower limb motion *** this approach overcomes the limitations of human feature engineering,it may ignore the potential correlation among the sEMG *** paper proposes a spatial–temporal graph neural network model,STGNN-LMR,designed to address the problem of recognizing lower limb motion from multi-channel ***-LMR transforms multi-channel sEMG into a graph structure and uses graph learning to model spatial–temporal *** 8-channel sEMG dataset is constructed for the experimental stage,and the results show that the STGNN-LMR model achieves a recognition accuracy of 99.71%.Moreover,this paper simulates two unexpected scenarios,including sEMG sensors affected by sweat noise and sudden failure,and evaluates the testing results using hypothesis *** to the experimental results,the STGNN-LMR model exhibits a significant advantage over the control models in noise scenarios and failure *** experimental results confirm the effectiveness of the STGNN-LMR model for addressing the challenges associated with sEMG-based lower limb motion recognition in practical scenarios.

关键词： Lower limb motion recognition Exoskeleton sEMG.Graph neural network Noise Sensor failure

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Hot Deformation Behavior and Workability of a New Ni–W–Cr Superalloy for Molten Salt Reactors

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Acta Metallurgica Sinica(English Letters) 2024年第8期37卷 1453-1466页

作者： Long Liu Zijian Zhou Jie yu Xinguang Wang Chuanyong Cui Rui Zhang yizhou Zhou Xiaofeng Sun Shi‑Changxu Innovation Center for Advanced Materials Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China School of Materials Science and Engineering University of Science and Technology of ChinaShenyang 110016China Fushun Special Steel Co. LtdFushun 113000China

The hot deformation behavior of a newly developed Ni–W–Cr superalloy for use in 800℃molten salt reactors(MSRs)was looked into by isothermal compression tests in the temperature range of 1050–1200℃with a strain rate of 0.001–1 s^(−1)under a true strain of *** Arrhenius-type model for the Ni–W–Cr superalloy was constructed by fitting the corrected flow stress *** this model,the effect of dispersion of solid solution elements during thermal deformation on microstructure evolution was considered,as well as the effects of friction and adiabatic heating on the temperature and strain rate-dependent variation of flow *** hot deformation activation energy of the Ni–W–Cr superalloy was 323 kJ/mol,which was less than that of the Hastelloy N alloy(currently used in MSRs).According to the rectified flow stress data,processing maps were *** conjunction with the corresponding deformation microstructures,the flow instability domains of the Ni–W–Cr superalloy were determined to be 1050–1160℃/0.03–1 s^(−1)and 1170–1200℃/0.001–0.09 s^(−1).In these deformation conditions,a locally inhomogeneous microstructure was caused by flow-i.e.,incomplete dynamic recrystallization and hot working parameters should avoid sliding into these *** ideal processing hot deformation domain for the Ni–W–Cr superalloy was determined to be 1170–1200℃/0.6–1 s^(−1).

关键词： Ni-W-Cr Superalloy Hot deformation behavior Constitutive equation Processing map Microstructure evolution

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The application of artificial intelligence to chest medical image analysis

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Intelligent Medicine 2021年第3期1卷 104-117页

作者： Feng Liu Jie Tang Jiechao Ma Cheng Wang Qing Ha yizhou yu Zhen Zhou Deepwise AI Lab Deepwise Inc.No.8 Haidian AvenueBeijing 100080China Department of Computer Science The University of Hong KongPok Fu LamHong KongChina

The aim of this article is to review recent progress in the application of artificial intelligence to chest medical image *** lungs,bone,and mediastinum were included in terms of anatomy,while X-ray and computed tomography(CT),with and without contrast enhancement,were considered regarding imaging *** key components of deep learning were summarized,namely,network architectures,learning strategies,optimiza-tion methods,and vision ***-specific applications were discussed in detail with respect to the dimen-sion of the data input,network architecture,and modality:lung cancer,pneumonia,tuberculosis,pulmonary embolism,chronic obstructive pulmonary disease,and interstitial lung disease for lung;traumatic fractures,os-teoporosis,osteoporotic fractures,and bone metastases for bone;and coronary artery calcification and aortic dissection for vascular ***,five promising research directions and possible solutions were presented for future work.

关键词： Artificial intelligence Deep learning Medical images Chest Lung Fracture Mediastinum

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规则库块状结构的自动生成

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计算机工程 1991年第6期6卷 15-20,65页

作者：俞益洲浙江大学

将规则库分块是加快规则库匹配的一个重要途径。本文先给出将规则库划分成若干独立子块的划分算法,接着又给出一种允许各子块间有一定相关性的星形分块法。在此基础上,作者提出了准点割集的概念,得到了若干有关定理及星形分块法的修正算... 详细信息

将规则库分块是加快规则库匹配的一个重要途径。本文先给出将规则库划分成若干独立子块的划分算法,接着又给出一种允许各子块间有一定相关性的星形分块法。在此基础上,作者提出了准点割集的概念,得到了若干有关定理及星形分块法的修正算法,最后作者还提出了最优星形结构概念,并给出了一个寻找规则库最优星形分块的算法,上述算法提供了由计算机自动对规则库进行划分的方法。

关键词：规则库块状结构算法自动生成

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