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Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence tomography

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光：科学与应用（英文版） 2025年第2期14卷 490-508页

作者： Shiyi Cheng Shuaibin Chang Yunzhe Li Anna Novoseltseva Sunni Lin Yicun Wu Jiahui Zhu Ann C.McKee Douglas L.Rosene Hui Wang Irving J.Bigio David A.Boas Lei Tian Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCA 94720USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA Department of Computer Science Boston UniversityBostonMA 02215USA Boston University Alzheimer's Disease Research Center and CTE Center Boston University School of MedicineBostonMA 02118USA Department of Neurology Boston University School of MedicineBostonMA 02118USA VA Boston Healthcare System U.S.Department of Veteran AffairsBostonMA 02130USA Department of Pathology and Laboratory Medicine Boston University School of MedicineBostonMA 02118USA Department of Anatomy & Neurobiology Boston University School of MedicineBostonMAUSA Department of Radiology Athinoula A.Martinos Center for Biomedical ImagingMassachusetts General HospitalBostonMA 02129USA Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA Neurophotonics Center Boston UniversityBostonMA 02215USA

A major challenge in neuroscience is visualizing the structure of the human brain at different *** histology reveals micro-and meso-scale brain features but suffers from staining variability,tissue damage,and distortion,which impedes accurate 3D *** emerging label-free serial sectioning optical coherence tomography(S-OCT)technique offers uniform 3D imaging capability across samples but has poor histological interpretability despite its sensitivity to cortical ***,we present a novel 3D imaging framework that combines S-OCT with a deep-learning digital staining(DS)*** enhanced imaging modality integrates high-throughput 3D imaging,low sample variability and high interpretability,making it suitable for 3D histology *** develop a novel semi-supervised learning technique to facilitate DS model training on weakly paired images for translating S-OCT to Gallyas silver *** demonstrate DS on various human cerebral cortex samples,achieving consistent staining quality and enhancing contrast across cortical layer ***,we show that DS preserves geometry in 3D on cubic-centimeter tissue blocks,allowing for visualization of meso-scale vessel networks in the white *** believe that our technique has the potential for high-throughput,multiscale imaging of brain tissues and may facilitate studies of brain structures.

关键词： tomography serial imaging digital human brain coherence enhanced multiscale optical

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Automatic detection and classification algorithm of thyroid nodules in CT images based on YOLOv5s 9

Automatic detection and classification algorithm of thyroid ...

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9th International Conference on computer and Communications, ICCC 2023

作者： Guo, Ruifeng Xu, Lei Guo, Jiahui Wang, Hongliang Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Shenyang China Liaoning Prov. Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China The First Hospital of China Medical University Department of Endocrinology and Metabolism Shenyang China

ISBN: (纸本)9798350317251

Thyroid nodular lesions are one of the most common diseases in the population. As the number of patients increases, it is clinically important to provide physicians with an accurate early aid to determine the benignity and malignancy of thyroid nodules in order to avoid unnecessary surgery and reduce patient burden. In this paper, we propose an improved Yolov5s-based target detection algorithm for benign and malignant thyroid nodules for thyroid nodule classification and identification. Replacing the backbone network of Yolov5s with ResNet18 to reduce the complexity of the model and the number of parameters;introducing the CBAM attention mechanism into the Yolov5s algorithm to improve the ability to extract features;Modify the spatial pyramid pooling module SPP to SimSPPF to improve the training speed of the algorithm;replace GIoU Loss with CIoU-Focal Loss as the loss function of the algorithm to improve the localization accuracy while increasing the regression rate of the bounding box. The experimental results show that the improved Yolov5s achieved an average accuracy of 92.9%, which is 2.9 percentage points higher than the average accuracy of the original algorithm, and can effectively improve the accuracy and efficiency of nodule detection. © 2023 IEEE.

关键词： computerized tomography

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Characterizing Probabilistic Structure in Learning Using Information Sufficiency

Characterizing Probabilistic Structure in Learning Using Inf...

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IEEE Workshop on Machine Learning for Signal Processing

作者： Victor Faraggi Jorge F. Silva Camilo Ramírez Alvaro Egaña Eduardo Pavez Information and Decision System Group Universidad de Chile Advanced Laboratory for Geostatistical Supercomputing Universidad de Chile Department of Electrical and Computer Engineering University of Southern California

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

We use the concept of information sufficiency (IS) to represent probabilistic structures in machine learning (ML). Our main result provides a functional expression that charac-terizes the class of probabilistic models consistent with an IS encoder-decoder latent predictive structure. This result formally justifies the encoder-decoder forward stages many modern ML architectures adopt to learn latent (compressed) representations in data. To illustrate IS as a realistic and rele-vant model assumption, we revisit some known ML concepts and present some interesting new examples: invariant, robust, sparse, and digital models.

关键词： Conferences Machine learning Signal processing Predictive models Probabilistic logic

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SSC- l0 : Sparse Subspace Clustering with the l0 Inequality Constraint 7th

SSC- l0 : Sparse Subspace Clustering with the l0 Inequality...

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Wang, Yangbo Zhou, Jie Lin, Qingshui Lu, Jianglin Gao, Can National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Basic Teaching Department Liaoning Technical University Huludao125105 China College of Engineering Northeastern University BostonMA02115 United States

ISBN: (纸本)9783031476648

Self-expression learning methods often obtain a coefficient matrix to measure the similarity between pairs of samples. However, directly using all points to represent a fixed sample in a class under the self-expression framework may not be ideal, as points from other classes participate in the representing process. To alleviate this issue, this study attempts to achieve representation learning between points only coming from the same class. In practice, it is easier for data points from the same class to represent each other than that from different classes. So, when reconstructing a point, if the number of non-zero elements in the coefficient vector is limited, a model is more likely to select data points from the class where the reconstructed point lies to complete the reconstruction work. Based on this idea, we propose Sparse Subspace Clustering with the l0 inequality constraint (SSC- l0 ). In SSC- l0, the l0 inequality constraint determines the maximum number of non-zero elements in the coefficient vector, which helps SSC- l0 to conduct representation learning among the points in the same class. After introducing the simplex constraint to ensure the translation invariance of the model, an optimization method concerning l0 inequality constraint is formed to solve the proposed SSC- l0, and its convergence is theoretically analyzed. Extensive experiments on well-known datasets demonstrate the superiority of SSC- l0 compared to several state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Learning systems

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Applying system Thinking to Learn from Accident of Modern Automatic Control systems

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Chinese Journal of Electronics 2025年第2期23卷 409-414页

作者： Ru Niu Yuan Cao Xiaocheng Ge Tao Tang State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China School of Electronics and Information Engineering National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China Department of Computer Science University of York York UK

Modern societies increasingly rely on automatic control systems. These systems are hardly pure technical systems; instead they are complex socio-technical systems, which consist of technical elements and social components. It is necessary to have a systematic approach to analyze these systems because it is growing evidence that accidents from these systems usually have complex causal factors which form an interconnected network of events, rather than a simple cause-effect chain. We take railway Train control systems (TCS) as an example to demonstrate the importance of the socio-technical approach to analyze the system. The paper presents an investigation of recent high-speed railway accident by applying STAMP - one of the most notable socio-technical system analysis techniques, outlines improvements to the system which could avoid similar accidents in the future. We also provide our valuable feedback for the use of STAMP.

关键词： Sociotechnical systems Analytical models systematics Railway accidents Transportation Medical services Rail transportation systems thinking Research and development Accidents

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Personalized Federated Learning with Enhanced Implicit Generalization

Personalized Federated Learning with Enhanced Implicit Gener...

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International Joint Conference on Neural Networks (IJCNN)

作者： Heping Liu Songbai Liu Junkai Ji Qiuzhen Lin Jianyong Chen Kay Chen Tan College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Integrating personalization into federated learning is crucial for addressing data heterogeneity and surpassing the limitations of a single aggregated model. Personalized federated learning excels at capturing inter-client similarities and meeting diverse client needs through custom-made models. However, even with personalized approaches, it’s essential to aggregate knowledge among clients to ensure universal benefits. This paper proposes Federated Dual Objectives and Dual Models (FedDodm), a novel approach that employs two independent models to separately address explicit personalization and implicit generalization objectives in personalized federated learning. By treating these objectives as distinct loss functions and training models accordingly, we achieve a balance between the two through a fusion method. Extensive experiments across various models and learning tasks demonstrate that FedDodm outperforms state-of-the-art federated learning approaches, marking a significant advancement in effectively integrating personalized and generalized knowledge.

关键词： Training Federated learning Aggregates Neural networks Data models

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Multi Criteria Decision Making Technique for Evaluation and Selection performance Large Scale Data of Composite Materials

Multi Criteria Decision Making Technique for Evaluation and ...

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2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2022

作者： Jassim Al-Shamary, Aidel Kadum Yas, Qahtan M. Badr, Aymen Mudheher Shalabi, Riyad Al Aldulaimi, Saeed Hameed Uninversity of Diyala Department of Mechanical Engineering Baqubah Iraq University of Diyala Department of Computer Science Baqubah Iraq Digital Research Center of Sfax Laboratory of Signals SysteMs ARtificial Intelligence and Networks Sfax University Sfax Tunisia Bahrain

ISBN: (数字)9781665469197

ISBN: (纸本)9781665469197

the composite material is considered basic material in various fields nowadays. Many companies have produced different types of this product according to specific criteria. This paper aims to present a new methodology for evaluation and selection based on multi criteria decision making (MCDM) techniques. A novelty claims by using an evaluation matrix (EM) to evaluate sandwich composite materials performance. The main issue of the evaluation of sandwich composite materials is performed by finding suitable criteria for performance metrics and the trade-off among the different types of composite materials. The methodology implemented in two steps;1) Creation decision matrix by calculating the data based on the five alternatives, 2) MCDM approach adapted by integrating between AHP and TOPSIS techniques. The finding outcome of the comparison of external with internal aggregation values to select the ideal sandwich composite materials was 5mm/5mm/5mm sample at 10J of threshold which is most recommended, whereas the 15mm sample at 25J of threshold obtained the lowest value, and it is the worst case. © 2022 IEEE.

关键词： Decision making

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Few-shot RUL Prediction with A Hypernetwork Structure Incorporating Uncertainty Quantification and Calibration

Few-shot RUL Prediction with A Hypernetwork Structure Incorp...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Ying Wang Fangyu Li Di Wang Department of Industrial Engineering and Management School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education and Beijing Laboratory for Urban Mass Transit Beijing University of Technology Beijing China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Deep learning (DL) has emerged as a powerful tool for predicting the remaining useful lifetime (RUL) of components and systems. However, there are two challenges. First, DL-based methods require a sufficient number of labelled samples, while the number of run-to-failure units in practical is often small due to the high cost and time-consuming life test. Second, it is desirable to provide the prediction uncertainty of RUL for maintenance decision-making. To tackle above issues, we propose a novel few-shot RUL prediction model with a hypernetwork structure incorporating uncertainty quantification and calibration (Hyper-UC). The proposed Hyper-UC uses a shared feature embedding network and a unit-specific weight to model the mapping from sensor signals to RUL, and a generative network is designed to learn the meta-knowledge of generating distributions over the unit-specific weights. To accurately provide the predictive uncertainty, the Hyper-UC systematically models two types of uncertainty: epistemic uncertainty and aleatoric uncertainty. In the online phase, the unit-specific weights of in-service units are obtained through calibration, and subsequently the predictive distribution of the RUL can be obtained. The proposed method is evaluated to have superior model performance than the benchmark methods in a case study using the C-MAPSS dataset.

关键词： Deep learning Uncertainty Costs computer aided software engineering Decision making Predictive models Benchmark testing Calibration Maintenance Prognostics and health management

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LucidGrasp: Robotic Framework for Autonomous Manipulation of laboratory Equipment with Different Degrees of Transparency via 6D Pose Estimation

LucidGrasp: Robotic Framework for Autonomous Manipulation of...

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IEEE International Conference on Robotics and Biomimetics

作者： Maria Makarova Daria Trinitatova Qian Liu Dzmitry Tsetserukou Intelligent Space Robotics Laboratory Center for Digital Engineering Skolkovo Institute of Science and Technology (Skoltech) Moscow Russia Department of Computer Science and Technology Dalian University of Technology China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Many modern robotic systems operate autonomously, however they often lack the ability to accurately analyze the environment and adapt to changing external conditions, while teleoperation systems often require special operator skills. In the field of laboratory automation, the number of automated processes is growing, however such systems are usually developed to perform specific tasks. In addition, many of the objects used in this field are transparent, making it difficult to analyze them using visual channels. The contributions of this work include the development of a robotic framework with autonomous mode for manipulating liquid-filled objects with different degrees of transparency in complex pose combinations. The conducted experiments demonstrated the robustness of the designed visual perception system to accurately estimate object poses for autonomous manipulation, and confirmed the performance of the algorithms in dexterous operations such as liquid dispensing. The proposed robotic framework can be applied for laboratory automation, since it allows solving the problem of performing non-trivial manipulation tasks with the analysis of object poses of varying degrees of transparency and liquid levels, requiring high accuracy and repeatability.

关键词： Visualization Liquids Automation Accuracy Biomimetics Pose estimation Robustness Robots Visual perception

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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