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FASNet: Feature alignment-based method with digital pathology images in assisted diagnosis medical system

Heliyon 2024年第22期10卷 e40350页

作者： He, Keke Zhu, Jun Li, Limiao Gou, Fangfang Wu, Jia School of Computer Science and Engineering Changsha University Changsha 410003 China Hunan University of Medicine General Hospital Huaihua 418000 China Collaborative Innovation Center for Medical Artificial Intelligence and Big Data Decision Making Assistance Hunan University of Medicine Huaihua 418000 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang 550025 China Research Center for Artificial Intelligence Monash University Melbourne Clayton 3800 VIC Australia

Many important information in medical research and clinical diagnosis are obtained from medical images. Among them, digital pathology images can provide detailed tissue structure and cellular information, which has become the gold standard for clinical tumor diagnosis. With the development of neural networks, computer-aided diagnosis presents the identification results of various cell nuclei to doctors, which facilitates the identification of cancerous regions. However, deep learning models require a large amount of annotated data. Pathology images are expensive and difficult to obtain, and insufficient annotation data can easily lead to biased results. In addition, when current models are evaluated on an unknown target domain, there are errors in the predicted boundaries. Based on this, this study proposes a feature alignment-based detail recognition strategy for pathology image segmentation (FASNet). It consists of a preprocessing model and a segmentation network (UNW). The UNW network performs instance normalization and categorical whitening of feature images by inserting semantics-aware normalization and semantics-aware whitening modules into the encoder and decoder, which achieves the compactness of features of the same class and the separation of features of different classes. The FASNet method can identify the feature detail information more efficiently, and thus differentiate between different classes of tissues effectively. The experimental results show that the FASNet method has a Dice Similarity Coefficient (DSC) value of 0.844. It achieves good performance even when faced with test data that does not match the distribution of the training data. Code: https://***/zlf010928/***. © 2024

关键词： Assisted diagnosis Deep learning Digital pathology images Feature alignment Insufficient annotation sets

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Cross-Category Prediction of Corrosion Inhibitor Performance Based on Molecular Graph Structures Via a Three-Level Message Passing Neural Network Model

SSRN

引用

SSRN 2022年

作者： Dai, Jiaxin Fu, Dongmei Song, Guangxuan Ma, Lingwei Guo, Xin Mol, Arjan Cole, Ivan Zhang, Dawei School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials & Technology University of Science and Technology Beijing Beijing China National Materials Corrosion and Protection Data Center University of Science and Technology Beijing Beijing China Department of Materials Science and Engineering Delft University of Technology Delft Netherlands School of Engineering RMIT University Melbourne Australia

Current experimental verification, computational modeling, and machine learning methods for predicting corrosion inhibition efficiency (IE) are limited to specific inhibitor categories with high cost and poor generalization. In this study, a cross-category corrosion inhibitor dataset is constructed and a three-level direct message passing neural network (3L–DMPNN) model using molecular structure information that integrates atomic-level, chemical bond-level, and molecular-level features to predict the IEs of compounds in a specific environment is established. This work demonstrates that the 3L–DMPNN model can predict IEs of cross-category corrosion inhibitors from other independent literature and experimental dataset effectively and quickly. © 2022, The Authors. All rights reserved.

关键词： Corrosion inhibitors

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Quantum Graph Convolutional Neural Networks

Quantum Graph Convolutional Neural Networks

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第40届中国控制会议

作者： Jin Zheng Qing Gao Yanxuan Lü School of Automation Science and Electrical Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

At present, there are a large number of quantum neural network models to deal with Euclidean spatial data, while little research have been conducted on non-Euclidean spatial data. In this paper, we propose a novel quantum graph convolutional neural network（QGCN） model based on quantum parametric circuits and utilize the computing power of quantum systems to accomplish graph classification tasks in traditional machine learning. The proposed QGCN model has a similar architecture as the classical graph convolutional neural networks, which can illustrate the topology of the graph type data and efficiently learn the hidden layer representation of node features as well. Numerical simulation results on a graph dataset demonstrate that the proposed model can be effectively trained and has good performance in graph level classification tasks.

关键词： Quantum machine learning Quantum computing Quantum graph convolutional neural networks

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Dear-DIA^(XMBD): Deep Autoencoder Enables Deconvolution of data-Independent Acquisition Proteomics

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Research 2024年第1期6卷 707-720页

作者： Qingzu He Chuan-Qi Zhong Xiang Li Huan Guo Yiming Li Mingxuan Gao Rongshan Yu Xianming Liu Fangfei Zhang Donghui Guo Fangfu Ye Tiannan Guo Jianwei Shuai Jiahuai Han Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China Oujiang Laboratory(Zhejiang Lab for Regenerative Medicine Vision and Brain Health)and Wenzhou InstituteUniversity of Chinese Academy of SciencesWenzhouZhejiang 325001China School of Life Sciences Xiamen UniversityXiamen 361102China State Key Laboratory of Cellular Stress Biology Innovation Center for Cell Signaling NetworkXiamen 361102China Department of Computer Science Xiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine School of MedicineXiamen UniversityXiamen 361102China Bruker(Beijing)Scientific Technology Co.Ltd. BeijingChina Westlake Laboratory of Life Sciences and Biomedicine Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake University18 Shilongshan RoadHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced Study18 Shilongshan RoadHangzhou 310024China Westlake Omics Ltd. Yunmeng Road 1HangzhouChina Department of Electronic Engineering Xiamen UniversityXiamen 361005China

data-independent acquisition(DIA)technology for protein identification from mass spectrometry and related algorithms is developing *** spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising *** this paper,we proposed an untargeted analysis method,Dear-DIA^(XMBD),for direct analysis of DIA ***-DIA^(XMBD) first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms,then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes,and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and *** show that Dear-DIA^(XMBD) performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms.

关键词： Deep Auto Independent

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Application and Implementation of Traffic Violation Report Combined with Vision and Voice Kits

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Sensors and Materials 2021年第2期34卷 533-543页

作者： Wu, Wen-Chuan Chen, Yen-Lun Lin, Pei-Yu Department of Computer Science and Information Engineering National Ilan University No. 1 Sec. 1 Shennong Rd. Yilan County 260 Yilan Taiwan Department of Computer Science and Information Engineering Aletheia University No. 32 Zhen-Li St. Tamsui Dist. New Taipei City25103 Taiwan Department of Information Communication Innovation Center for Big Data and Digital Convergence Yuan Ze University No. 135 Yuan-Tung Rd. Chung-Li32003 Taiwan

The Internet of Things (IoT) is a technique to connect various devices together in a network and exchange data among themselves. Currently, there are many IoT applications, such as smart homes, smart cities, smart manufacturing, and so forth. Their purpose is to make life more convenient. In this paper, we use IoT technology to solve the problem of traffic violation reports. We utilize specific sensors and a signal processing technique to detect events or changes in the environment. Then, messages are sent to trigger a camera to obtain videos of the violation. These video clips and the related information are finally uploaded to the cloud space of the reporter of the violation. Experimental results showed that the proposed scheme can speed up traffic violation reporting and can increase the willingness of people to act as whistleblowers. © 2021 M Y U Scientific Publishing Division. All rights reserved.

关键词： Automation

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Video-based automatic Early Parkinson's disease detection system using biomechanical features 30th

Video-based automatic Early Parkinson's disease detection sy...

引用

30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020

作者： Lin, Changhong Wang, Shaoping School of Automation Science and Electrical Engineering Beihang University China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University China

ISBN: (纸本)9789811485930

Changes in biomechanical features are an important early symptom of Parkinson's disease(PD). Intending to make Parkinson's disease diagnosis easier and more accessible, this paper presents a marker-less system that performs automatic detection and classification of early Parkinson's disease, based on the evaluation and inference of biomechanical features using a single 2D video camera and naive Bayesian algorithm.9 biomechanical features are selected according to the statistic analysis result of Parkinson's disease and Unified Parkinson Disease Rating Scale (UPDRS), including two types of features, one is spatial biomechanical features(step length, range of joint motion, etc.) another is temporal biomechanical features(cadence and gait cycle phase). Movement information of individual joints is extracted from videos of subjects' sagittal plane using Openpose, which is a pose estimation algorithm built with deep learning. Parameters of the Bayesian classification model are estimated by symptom statistics of Parkinson's disease to avoid a lack of generalization of the classification model due to small sample sizes. Experiment results show that the achieved accuracy of the probabilistic classification was 95.7%. © ESREL2020-PSAM15 Organizers.

关键词： Video cameras

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Curvature Graph Generative Adversarial Networks

arXiv

引用

arXiv 2022年

作者： Li, Jianxin Fu, Xingcheng Sun, Qingyun Ji, Cheng Tan, Jiajun Wu, Jia Peng, Hao Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China School of Computing Macquarie University Sydney Australia

Generative adversarial network (GAN) is widely used for generalized and robust learning on graph data. However, for non-Euclidean graph data, the existing GAN-based graph representation methods generate negative samples by random walk or traverse in discrete space, leading to the information loss of topological properties (e.g. hierarchy and circularity). Moreover, due to the topological heterogeneity (i.e., different densities across the graph structure) of graph data, they suffer from serious topological distortion problems. In this paper, we proposed a novel Curvature Graph Generative Adversarial Networks method, named CurvGAN, which is the first GAN-based graph representation method in the Riemannian geometric manifold. To better preserve the topological properties, we approximate the discrete structure as a continuous Riemannian geometric manifold and generate negative samples efficiently from the wrapped normal distribution. To deal with the topological heterogeneity, we leverage the Ricci curvature for local structures with different topological properties, obtaining to low-distortion representations. Extensive experiments show that CurvGAN consistently and significantly outperforms the state-of-the-art methods across multiple tasks and shows superior robustness and generalization. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Adaptive Leaderless Consensus for a Class of Uncertain Nonlinear Systems with Intermittent Actuator Faults* 15

Adaptive Leaderless Consensus for a Class of Uncertain Nonli...

引用

15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020

作者： Mahdi Al-Adhami, Marwah Saad Long, Jiang Wang, Wei Wang, Lei Beihang University School of Automation Science and Electrical Engineering Beijing China Beihang University School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China

ISBN: (纸本)9781728151694

This paper investigates the leaderless consensus problem for high-order nonlinear systems with unmatched unknown parameters and intermittent actuator faults. Under directed topology condition, a novel adaptive fault tolerant leaderless consensus control scheme is proposed. In order to achieve the leaderless consensus under directed topology condition, an auxiliary system, which is related to the individual states and neighboring states, is introduced in each agent. On the other hand, to compensate the effect of intermittent actuator faults on the system stability of each agent, a local parameter estimator is introduced to estimate a lumped unknown parameter related to the lower bound of the effectiveness of actuator. It is shown that with the proposed adaptive fault tolerant leaderless consensus control scheme, all the closed-loop signals are globally uniformly bounded and all the agents' outputs can reach a consensus. © 2020 IEEE.

关键词： System stability

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Bayesian filter observers based estimation of glucose and insulin concentration in plasma 30th

Bayesian filter observers based estimation of glucose and in...

引用

30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM15 2020

作者： Wang, Weijie Wang, Shaoping Wang, Xingjian Geng, Yixuan School of Automation Science and Electrical Engineering Beihang University China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University China

ISBN: (纸本)9789811485930

It is the prerequisite for artificial pancreas (AP) to predict the excursion of glucose and insulin concentrations in the plasma, while only the subcutaneous glucose levels can be measured in real-time by Continuous Glucose Monitoring System (CGMS). The purpose of this study is to predict glucose and insulin concentrations excursion in plasma properly, only using CGMS data and insulin infusion rate data. Firstly, Ruan model, a glucose-insulin model is introduced to build a state space to describe the dynamics of insulin transportation, glucose-insulin interaction,and glucose transportation, taking the carbohydrate intake into consideration. Then, considering the inter-and intra-individual variability, we incorporate parameter uncertainty to describe the high variability, and extend the observable parameters to extended state variable, which can be updatated according to the real-time collected data. Next, three types of Bayesian filter observers, Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF) are designed to track all the states dynamically. Finally, based on the updated states, the glucose and insulin concentrations in plasma are estimated and predicted. We evaluate this methodology with an in-silico study with 30 Type-1 diabetes patients during one week. It is proven that our method can track the excursion of glucose and insulin concentrations in the plasma effectively. Moreover, the PF outperforms other two filters in predicting the glucose and insulin concentrations, with MSE of 1.56±0.69 mmol2 /L2 and 11.70±3.14 mU2 /L2 respectively. © ESREL2020-PSAM15 *** by Research Publishing, Singapore.

关键词： Glucose

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Evaluating Pre-trained Convolutional Neural Networks and Foundation Models as Feature Extractors for Content-based Medical Image Retrieval

arXiv

引用

arXiv 2024年

作者： Mahbod, Amirreza Saeidi, Nematollah Hatamikia, Sepideh Woitek, Ramona Research Center for Medical Image Analysis and Artificial Intelligence Department of Medicine Faculty of Medicine and Dentistry Danube Private University Krems an der Donau Austria Artificial Intelligence Department Faculty of Computer Engineering University of Isfahan Isfahan Iran Research Center for Clinical AI-Research in Omics and Medical Data Science Department of Medicine Faculty of Medicine and Dentistry Danube Private University Krems an der Donau Austria Austrian Center for Medical Innovation and Technology Wiener Neustadt Austria

Medical image retrieval refers to the task of finding similar images for given query images in a database, with applications such as diagnosis support. While traditional medical image retrieval relied on clinical metadata, content-based medical image retrieval (CBMIR) depends on image features, which can be extracted automatically or semi-automatically. Many approaches have been proposed for CBMIR, and among them, using pre-trained convolutional neural networks (CNNs) is a widely utilized approach. However, considering the recent advances in the development of foundation models for various computer vision tasks, their application for CBMIR can also be investigated. In this study, we used several pre-trained feature extractors from well-known pre-trained CNNs and pre-trained foundation models and investigated the CBMIR performance on eight types of two-dimensional (2D) and three-dimensional (3D) medical images. Furthermore, we investigated the effect of image size on the CBMIR performance. Our results show that, overall, for the 2D datasets, foundation models deliver superior performance by a large margin compared to CNNs, with the general-purpose self-supervised model for computational pathology (UNI) providing the best overall performance across all datasets and image sizes. For 3D datasets, CNNs and foundation models deliver more competitive performance, with contrastive learning from captions for histopathology model (CONCH) achieving the best overall performance. Moreover, our findings confirm that while using larger image sizes (especially for 2D datasets) yields slightly better performance, competitive CBMIR performance can still be achieved even with smaller image sizes. Our codes to reproduce the results are available at: https://***/masih4/MedImageRetrieval. © 2024, CC BY-NC-ND.

关键词： Convolutional neural networks

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