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Article
Clinical Neurology
Guanzhang Li et al.
Summary: Using pre-operative T2-weighted MRI data from glioma patients, a radiomics model was developed by Li et al. that accurately predicts overall survival. The model uncovers associations between radiomic and molecular features, particularly tumor macrophage infiltration.
Correction
Oncology
Quinn T. Ostrom et al.
Article
Computer Science, Interdisciplinary Applications
Jianhong Cheng et al.
Summary: Accurate prediction of isocitrate dehydrogenase (IDH) mutation and glioma segmentation is crucial for computer-aided diagnosis using preoperative multimodal magnetic resonance imaging (MRI). Existing methods often address these tasks separately and overlook the correlation between them. Furthermore, the limited availability of IDH mutation data hampers modeling. In this paper, we propose a fully automated multimodal MRI-based multi-task learning framework that simultaneously performs glioma segmentation and IDH genotyping. Our framework combines a hybrid CNN-Transformer encoder with a multi-scale classifier to extract shared spatial and global information for segmentation and genotyping. A multi-task learning loss is designed to balance the two tasks, while an uncertainty-aware pseudo-label selection method improves the accuracy of IDH genotyping with semi-supervised learning. Experimental results on a multi-institutional public dataset demonstrate the promising performance of our proposed method compared to single-task learning and existing state-of-the-art methods. The availability of unlabeled data further enhances the performance of glioma segmentation and IDH genotyping. The source codes of our framework are publicly available.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Automation & Control Systems
Wen Li et al.
Summary: This study addresses the consensus tracking problem for second-order multi-agent systems under channel fading by introducing an event-triggered sliding mode controller to reduce network burden. The statistical characteristic of channel fading is integrated into the measurement function to mitigate the impact on transmission among followers. Theoretical analysis confirms the reachability and stability of the multi-agent system, while also demonstrating the event triggering strategy's ability to eliminate Zeno behavior. A simulation example is provided for verification.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Computer Science, Information Systems
Mostafa Soleymanifard et al.
Summary: This paper proposes a three-stage model for automatic segmentation of brain tumour sub-regions in multi-modal MRI images. The model combines a neural network and active contour model for segmenting the whole tumour tissue, and uses a fuzzy clustering-based approach for segmenting the enhancing tumours. Features are extracted using local binary pattern and gray-level co-occurrence matrix, and a neural network classifier is used to determine the grade of the tumours. Experimental results show that the proposed model is highly competitive in terms of segmentation accuracy.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Neurosciences
Lucian Marginean et al.
Summary: This study investigates the potential of computed tomography (CT)-based texture analysis (TA) in differentiating between high-grade gliomas (HGGs) and solitary brain metastases (BMs). The results show that TA features can successfully distinguish between the two entities, with HGGs showing a more heterogeneous content in the peritumoral zone (PZ) possibly due to neoplastic cell infiltration.
Article
Biology
Gunjan Pahuja et al.
Summary: Early and accurate diagnosis is crucial for the management and treatment of Parkinson's disease. This study proposes two deep learning models based on multi-modal features, using neuroimaging and biological features, to classify subjects into PD and healthy groups. The results demonstrate the effectiveness of the multi-modal approach in diagnosing Parkinson's disease.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Automation & Control Systems
Guangyan Bao et al.
Summary: This paper surveys the cooperative control of heterogeneous multi-agent systems (HMASs) subject to specific constraints. HMASs are classified into weak and strong categories based on different cooperative behaviors, and control strategies are discussed for dealing with various constraints on agent dynamics and communication networks. The latest results on cooperative control under different constraints are summarized, and conclusions are drawn along with possible future research directions.
SYSTEMS SCIENCE & CONTROL ENGINEERING
(2022)
Article
Automation & Control Systems
Han Li et al.
Summary: This paper reviews the use of mathematical tools to enhance LFIA performance, and proposes a novel taxonomy. It also presents the outlook of developing POCT in conjunction with other state-of-the-art techniques, and highlights the importance of applying computational intelligence methods in boosting POCT development.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Han Li et al.
Summary: In the context of the global COVID-19 pandemic, a computer aided diagnosis model called Cov-Net is proposed for accurate recognition of COVID-19 from chest X-ray images. Experimental results demonstrate the high feasibility and accuracy of the model in identifying COVID-19. Compared to other algorithms, Cov-Net exhibits superior performance and competitiveness in this task.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Automation & Control Systems
Nianyin Zeng et al.
Summary: In this article, a dynamic-neighborhood-based switching PSO (DNSPSO) algorithm is proposed with improved velocity update mechanism and learning strategy. The differential evolution algorithm is successfully hybridized with the particle swarm optimization algorithm to enhance the solution accuracy for multimodal optimization problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Biology
Han Li et al.
Summary: In this paper, a feature learning enhanced convolutional neural network (FLE-CNN) is proposed for cancer detection from histopathology images. Experimental results demonstrate the merits of the proposed FLE-CNN in terms of feature extraction, achieving improved performance compared to other advanced deep learning models.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Engineering, Electrical & Electronic
Nianyin Zeng et al.
Summary: In this article, a novel enhanced multiscale feature fusion method called ABFPN is proposed to improve the detection performance of small objects. It is evaluated on benchmark datasets and applied to detect surface defects on printed circuit boards. Experimental results demonstrate its reliability and efficiency.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Computer Science, Artificial Intelligence
Chompunuch Sarasaen et al.
Summary: The study introduces a super-resolution MRI reconstruction method that utilizes prior knowledge and fine-tuning to enhance spatial resolution of dynamic MRI images and reduce scanning time. Experimental results demonstrate that the method can significantly improve image quality while maintaining high similarity with ground-truth images.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2021)
Article
Automation & Control Systems
Jingyang Mao et al.
Summary: Recursive filtering for nonlinear systems is a core technology in modern industrial systems, facing challenges such as communication scheduling, limited bandwidth, and security vulnerability. It is of utmost significance in theory and great importance in applications to establish engineering-feasible recursive filtering algorithms for networked nonlinear systems. This paper provides an up-to-date survey of existing nonlinear filtering techniques and raises several challenging issues for further research and practical applications.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Automation & Control Systems
Jun Hu et al.
Summary: This paper reviews the latest state estimation schemes for complex dynamical networks (CDNs), covering various methods under different performance indices and focusing on protocol-based and compensation-based state estimation approaches. Challenging problems for future research are outlined to promote theoretical developments in related fields.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Review
Radiology, Nuclear Medicine & Medical Imaging
Bastian Zinnhardt et al.
Summary: Gliomas are common tumors of the central nervous system, with advances in genetic profiling and imaging technologies improving prognostic stratification and treatment decisions for patients. Amino acid PET and TSPO PET imaging have been widely used to study the heterogeneity and immune cell modulation of gliomas.
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
(2021)
Article
Automation & Control Systems
Lei Zou et al.
Summary: This work surveys the control and filtering problems of networked systems under the effects induced by communication protocols, introducing the engineering background and theoretical frameworks. It reviews recent advances in stability analysis, control, and filtering problems subject to protocol scheduling, as well as provides a review of fault diagnosis problems and outlines research challenges for future work in communication-protocol-based control and filtering.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Yuchen Fei et al.
Summary: The study aims to develop an algorithm for predicting target MRI sequences with high accuracy using a deep learning-based multi-modal computing model for MRI synthesis. The approach effectively utilizes complementary information from different modalities, leading to significant improvements in synthesis performance compared to benchmark methods.
Article
Oncology
Eve Donaldson et al.
Review
Oncology
David N. Louis et al.
Summary: The fifth edition of the WHO Classification of Tumors of the Central Nervous System integrates molecular diagnostics into CNS tumor classification, introduces different approaches to tumor nomenclature and grading, and emphasizes the importance of integrated diagnoses and layered reports.
Article
Chemistry, Multidisciplinary
Hiroto Yamashiro et al.
Summary: The proposed grading pipeline combines cloud-based trained 3D CNN and original 3D CNN for early patient treatment and prognosis prediction. Through evaluation, the grading accuracy of all tumors using this automated method reaches 91.3%, comparable to multi-sequence methods.
APPLIED SCIENCES-BASEL
(2021)
Article
Mathematical & Computational Biology
Hakan Ozcan et al.
Summary: A custom convolutional neural network (CNN) model was introduced in the study for effective glioma grade prediction, and compared with pretrained models using transfer learning, showing superior performance. The research highlights the effectiveness of deep CNNs and transfer learning approaches in solving classification problems in the medical domain.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2021)
Article
Automation & Control Systems
Peng Lu et al.
Summary: A new approach that combines convolutional neural network with augmented dataset is proposed in this paper to address the human face recognition issue on a small original dataset, achieving higher accuracy in face recognition by effectively extracting features from augmented face image dataset. This novel approach's effectiveness and superiority are confirmed through experiments and comparisons with commonly used face recognition methods.
SYSTEMS SCIENCE & CONTROL ENGINEERING
(2021)
Article
Automation & Control Systems
Yezheng Wang et al.
Summary: The paper investigates the complexities of T-S fuzzy control problems encountered in engineering practice, including traditional engineering-oriented complexities and network-induced complexities. Recent progress on T-S fuzzy control problems subject to these complexities has been reviewed in details, and conclusions have been drawn with possible future research directions proposed.
SYSTEMS SCIENCE & CONTROL ENGINEERING
(2021)
Article
Automation & Control Systems
Baoye Song et al.
Summary: This paper introduces a new workspace model to describe the environment of coal mines and develops an improved ant colony optimization algorithm for path planning of coal mine robots. Simulation experiments confirm the effectiveness and superiority of the new approach.
SYSTEMS SCIENCE & CONTROL ENGINEERING
(2021)
Article
Multidisciplinary Sciences
Ye Yuan et al.
NATIONAL SCIENCE REVIEW
(2020)
Article
Radiology, Nuclear Medicine & Medical Imaging
Zhiwei Zhang et al.
JOURNAL OF DIGITAL IMAGING
(2020)
Article
Radiology, Nuclear Medicine & Medical Imaging
Ying Zhuge et al.
Article
Computer Science, Artificial Intelligence
Elisa Ferrari et al.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2020)
Review
Engineering, Biomedical
Mina Ghaffari et al.
IEEE REVIEWS IN BIOMEDICAL ENGINEERING
(2020)
Article
Radiology, Nuclear Medicine & Medical Imaging
Jiwoong Jeong et al.
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
(2019)
Article
Multidisciplinary Sciences
Ye Yuan et al.
NATURE COMMUNICATIONS
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Muhaddisa Barat Ali et al.
COMPUTER ANALYSIS OF IMAGES AND PATTERNS, CAIP 2019, PT I
(2019)
Article
Neurosciences
Yang Yang et al.
FRONTIERS IN NEUROSCIENCE
(2018)
Article
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Ye Yuan et al.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Gao Huang et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
(2017)
Article
Neuroimaging
Rika Inano et al.
NEUROIMAGE-CLINICAL
(2014)
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Radiology, Nuclear Medicine & Medical Imaging
Frank G. Zoellner et al.
MAGNETIC RESONANCE IN MEDICINE
(2010)
