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Detection of thin-cap fibroatheroma in IVOCT images based on weakly supervised learning and domain knowledge SCIE Scopus
期刊论文 | 2023 | JOURNAL OF BIOPHOTONICS
SCOPUS Cited Count: 3
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Abstract :

Automatic detection of thin-cap fibroatheroma (TCFA) on intravascular optical coherence tomography images is essential for the prevention of acute coronary syndrome. However, existing methods need to mark the exact location of TCFAs on each frame as supervision, which is extremely time-consuming and expensive. Hence, a new weakly supervised framework is proposed to detect TCFAs using only image-level tags as supervision. The framework comprises cut, feature extraction, relation, and detection modules. First, based on prior knowledge, a cut module was designed to generate a small number of specific region proposals. Then, to learn global information, a relation module was designed to learn the spatial adjacency and order relationships at the feature level, and an attention-based strategy was introduced in the detection module to effectively aggregate the classification results of region proposals as the image-level predicted score. The results demonstrate that the proposed method surpassed the state-of-the-art weakly supervised detection methods.

Keyword :

deep learning optical coherence tomography the thin-cap fibroatheroma weakly supervised learning

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GB/T 7714 Shi, Peiwen , Xin, Jingmin , Wu, Jiayi et al. Detection of thin-cap fibroatheroma in IVOCT images based on weakly supervised learning and domain knowledge [J]. | JOURNAL OF BIOPHOTONICS , 2023 .
MLA Shi, Peiwen et al. "Detection of thin-cap fibroatheroma in IVOCT images based on weakly supervised learning and domain knowledge" . | JOURNAL OF BIOPHOTONICS (2023) .
APA Shi, Peiwen , Xin, Jingmin , Wu, Jiayi , Deng, Yangyang , Cai, Zhuotong , Du, Shaoyi et al. Detection of thin-cap fibroatheroma in IVOCT images based on weakly supervised learning and domain knowledge . | JOURNAL OF BIOPHOTONICS , 2023 .
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Multi-view Contour-constrained Transformer Network for Thin-cap Fibroatheroma Identification EI SCIE Scopus
期刊论文 | 2022 , 523 , 224-234 | NEUROCOMPUTING
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Abstract :

Identification and detection of thin-cap fibroatheroma (TCFA) from intravascular optical coherence tomography (IVOCT) images is critical for treatment of coronary heart diseases. Recently, deep learning methods have shown promising successes in TCFA identification. However, most methods usually do not effectively utilize multi-view information or incorporate prior domain knowledge. In this paper, we pro-pose a multi-view contour-constrained transformer network (MVCTN) for TCFA identification in IVOCT images. Inspired by the diagnosis process of cardiologists, we use contour constrained self-attention modules (CCSM) to emphasize features corresponding to salient regions (i.e., vessel walls) in an unsuper-vised manner and enhance the visual interpretability based on class activation mapping (CAM). Moreover, we exploit transformer modules (TM) to build global-range relations between two views (i.e., polar and Cartesian views) to effectively fuse features at multiple feature scales. Experimental results on a semi-public dataset and an in-house dataset demonstrate that the proposed MVCTN outperforms other single-view and multi-view methods. Lastly, the proposed MVCTN can also provide meaningful visualization for cardiologists via CAM.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keyword :

IVOCT Multi -view learning Plaque identification TCFA Transformer

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GB/T 7714 Liu, Sijie , Xin, Jingmin , Wu, Jiayi et al. Multi-view Contour-constrained Transformer Network for Thin-cap Fibroatheroma Identification [J]. | NEUROCOMPUTING , 2022 , 523 : 224-234 .
MLA Liu, Sijie et al. "Multi-view Contour-constrained Transformer Network for Thin-cap Fibroatheroma Identification" . | NEUROCOMPUTING 523 (2022) : 224-234 .
APA Liu, Sijie , Xin, Jingmin , Wu, Jiayi , Deng, Yangyang , Su, Ruisheng , Niessen, Wiro J. et al. Multi-view Contour-constrained Transformer Network for Thin-cap Fibroatheroma Identification . | NEUROCOMPUTING , 2022 , 523 , 224-234 .
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A-line-based thin-cap fibroatheroma detection with multi-view IVOCT images using multi-task learning and contrastive learning SCIE Scopus
期刊论文 | 2022 , 39 (12) , 2298-2306 | JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION
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Abstract :

Automatic detection of thin-cap fibroatheroma (TCFA) is essential to prevent acute coronary syndrome. Hence, in this paper, a method is proposed to detect TCFAs by directly classifying each A-line using multi-view intravascular optical coherence tomography (IVOCT) images. To solve the problem of false positives, a multi-input-output net -work was developed to implement image-level classification and A-line-based classification at the same time, and a contrastive consistency term was designed to ensure consistency between two tasks. In addition, to learn spatial and global information and obtain the complete extent of TCFAs, an architecture and a regional connectivity constraint term are proposed to classify each A-line of IVOCT images. Experimental results obtained on the 2017 China Computer Vision Conference IVOCT dataset show that the proposed method achieved state-of-art performance with a total score of 88.7 +/- 0.88%, overlap rate of 88.64 +/- 0.26%, precision rate of 84.34 +/- 0.86%, and recall rate of 93.67 +/- 2.29%.(c) 2022 Optica Publishing Group

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GB/T 7714 Shi, Peiwen , Xin, Jingmin , Zheng, Nanning . A-line-based thin-cap fibroatheroma detection with multi-view IVOCT images using multi-task learning and contrastive learning [J]. | JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION , 2022 , 39 (12) : 2298-2306 .
MLA Shi, Peiwen et al. "A-line-based thin-cap fibroatheroma detection with multi-view IVOCT images using multi-task learning and contrastive learning" . | JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION 39 . 12 (2022) : 2298-2306 .
APA Shi, Peiwen , Xin, Jingmin , Zheng, Nanning . A-line-based thin-cap fibroatheroma detection with multi-view IVOCT images using multi-task learning and contrastive learning . | JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION , 2022 , 39 (12) , 2298-2306 .
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Onboard Sensors-Based Self-Localization for Autonomous Vehicle With Hierarchical Map SCIE Scopus
期刊论文 | 2022 | IEEE TRANSACTIONS ON CYBERNETICS
SCOPUS Cited Count: 7
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Abstract :

Localization is a fundamental and crucial module for autonomous vehicles. Most of the existing localization methodologies, such as signal-dependent methods (RTK-GPS and Bluetooth), simultaneous localization and mapping (SLAM), and map-based methods, have been utilized in outdoor autonomous driving vehicles and indoor robot positioning. However, they suffer from severe limitations, such as signal-blocked scenes of GPS, computing resource occupation explosion in large-scale scenarios, intolerable time delay, and registration divergence of SLAM/map-based methods. In this article, a self-localization framework, without relying on GPS or any other wireless signals, is proposed. We demonstrate that the proposed homogeneous normal distribution transform algorithm and two-way information interaction mechanism could achieve centimeter-level localization accuracy, which reaches the requirement of autonomous vehicle localization for instantaneity and robustness. In addition, benefitting from hardware and software co-design, the proposed localization approach is extremely light-weighted enough to be operated on an embedded computing system, which is different from other LiDAR localization methods relying on high-performance CPU/GPU. Experiments on a public dataset (Baidu Apollo SouthBay dataset) and real-world verified the effectiveness and advantages of our approach compared with other similar algorithms.

Keyword :

Autonomous vehicle localization Autonomous vehicles homogeneous registration method Laser radar Location awareness normal distribution transform (NDT)-EKF tightly coupled algorithm Real-time systems Robustness Simultaneous localization and mapping Software algorithms software-hardware co-design

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GB/T 7714 Xia, Chao , Shen, Yanqing , Yang, Yuedong et al. Onboard Sensors-Based Self-Localization for Autonomous Vehicle With Hierarchical Map [J]. | IEEE TRANSACTIONS ON CYBERNETICS , 2022 .
MLA Xia, Chao et al. "Onboard Sensors-Based Self-Localization for Autonomous Vehicle With Hierarchical Map" . | IEEE TRANSACTIONS ON CYBERNETICS (2022) .
APA Xia, Chao , Shen, Yanqing , Yang, Yuedong , Deng, Xiaodong , Chen, Shitao , Xin, Jingmin et al. Onboard Sensors-Based Self-Localization for Autonomous Vehicle With Hierarchical Map . | IEEE TRANSACTIONS ON CYBERNETICS , 2022 .
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China's 12-Year Quest of Autonomous Vehicular Intelligence: The Intelligent Vehicles Future Challenge Program EI SCIE
期刊论文 | 2021 , 13 (2) , 6-19 | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
WoS CC Cited Count: 3
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Abstract :

In this article, we introduce the Intelligent Vehicles Future Challenge of China (IVFC), which has lasted 12 years. Some key features of the tests and a few interesting findings of IVFC are selected and presented. Through the IVFCs held between 2009 and 2020, we gradually established a set of theories, methods, and tools to collect tests' data and efficiently evaluate the performance of autonomous vehicles so that we could learn how to improve both the autonomous vehicles and the testing system itself.

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GB/T 7714 Wang, Fei-Yue , Zheng, Nan-Ning , Li, Li et al. China's 12-Year Quest of Autonomous Vehicular Intelligence: The Intelligent Vehicles Future Challenge Program [J]. | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE , 2021 , 13 (2) : 6-19 .
MLA Wang, Fei-Yue et al. "China's 12-Year Quest of Autonomous Vehicular Intelligence: The Intelligent Vehicles Future Challenge Program" . | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE 13 . 2 (2021) : 6-19 .
APA Wang, Fei-Yue , Zheng, Nan-Ning , Li, Li , Xin, Jingmin , Wang, Xiao , Xu, Linhai et al. China's 12-Year Quest of Autonomous Vehicular Intelligence: The Intelligent Vehicles Future Challenge Program . | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE , 2021 , 13 (2) , 6-19 .
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Exploring Highly Dependable and Efficient Datacenter Power System Using Hybrid and Hierarchical Energy Buffers EI Scopus CSSCI-E SCIE
期刊论文 | 2021 , 6 (3) , 412-426 | IEEE Transactions on Sustainable Computing
SCOPUS Cited Count: 2
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Abstract :

The massive and irregular load surges challenge datacenter power infrastructures. As a result, power mismatching between supply and demand has emerged as a crucial availability issue in modern datacenters which are either under-provisioned or powered by intermittent power sources. Recent proposals have employed energy storage devices such as the uninterruptible power supply(UPS) to address this issue. However, current approaches lack the capacity of efficiently handling the irregular and unpredictable power mismatches. In this paper, we propose Hybrid and Hierarchical Energy Buffering (HHEB), a novel heterogeneous and adaptive scheme that could enable various energy storage devices(ESDs) to be efficiently integrated into existing datacenters for dynamically dealing with power mismatches. Our techniques exploit the diverse characteristics of different ESDs and intelligent load assignment algorithms to improve the dependability and efficiency of datacenter power systems. We evaluate the HHEB design with a prototype. Compared with a homogenous battery energy buffering system, HHEB could improve energy efficiency by 39.7%, extend UPS lifetime by 4.7X, promote energy availability by 3.2X, reduce system downtime by 41% and effectively improve the energy availability of various energy buffers in different hierarchies. It allows datacenters to adapt to various power supply anomalies, thereby improving operational efficiency, dependability and availability.

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GB/T 7714 Longjun Liu , Hongbin Sun , Chao Li et al. Exploring Highly Dependable and Efficient Datacenter Power System Using Hybrid and Hierarchical Energy Buffers [J]. | IEEE Transactions on Sustainable Computing , 2021 , 6 (3) : 412-426 .
MLA Longjun Liu et al. "Exploring Highly Dependable and Efficient Datacenter Power System Using Hybrid and Hierarchical Energy Buffers" . | IEEE Transactions on Sustainable Computing 6 . 3 (2021) : 412-426 .
APA Longjun Liu , Hongbin Sun , Chao Li , Tao Li , Jingmin Xin , Nanning Zheng . Exploring Highly Dependable and Efficient Datacenter Power System Using Hybrid and Hierarchical Energy Buffers . | IEEE Transactions on Sustainable Computing , 2021 , 6 (3) , 412-426 .
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Efficient Repair Analysis Algorithm Exploration for Memory With Redundancy and In-Memory ECC EI SCIE
期刊论文 | 2021 , 70 (5) , 775-788 | IEEE TRANSACTIONS ON COMPUTERS
WoS CC Cited Count: 3
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Abstract :

In-memory error correction code (ECC) is a promising technique to improve the yield and reliability of high density memory design. However, the use of in-memory ECC poses a new problem to memory repair analysis algorithm, which has not been explored before. This article first makes a quantitative evaluation and demonstrates that the straightforward algorithms for memory with redundancy and in-memory ECC have serious deficiency on either repair rate or repair analysis speed. Accordingly, an optimal repair analysis algorithm that leverages preprocessing/filter algorithms, hybrid search tree, and depth-first search strategy is proposed to achieve low computational complexity and optimal repair rate in the meantime. In addition, a heuristic repair analysis algorithm that uses a greedy strategy is proposed to efficiently find repair solutions. Experimental results demonstrate that the proposed optimal repair analysis algorithm can achieve optimal repair rate and increase the repair analysis speed by up to 10(5) x compared with the straightforward exhaustive search algorithm. The proposed heuristic repair analysis algorithm is approximately 28 percent faster than the proposed optimal algorithm, at the expense of 5.8 percent repair rate loss.

Keyword :

in-memory ECC Memory repair reliability repair analysis algorithm yield

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GB/T 7714 Lv, Minjie , Sun, Hongbin , Xin, Jingmin et al. Efficient Repair Analysis Algorithm Exploration for Memory With Redundancy and In-Memory ECC [J]. | IEEE TRANSACTIONS ON COMPUTERS , 2021 , 70 (5) : 775-788 .
MLA Lv, Minjie et al. "Efficient Repair Analysis Algorithm Exploration for Memory With Redundancy and In-Memory ECC" . | IEEE TRANSACTIONS ON COMPUTERS 70 . 5 (2021) : 775-788 .
APA Lv, Minjie , Sun, Hongbin , Xin, Jingmin , Zheng, Nanning . Efficient Repair Analysis Algorithm Exploration for Memory With Redundancy and In-Memory ECC . | IEEE TRANSACTIONS ON COMPUTERS , 2021 , 70 (5) , 775-788 .
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DSP-Net: Dense-to-Sparse Proposal Generation Approach for 3D Object Detection on Point Cloud EI CPCI-S
会议论文 | 2021 | 2021 International Joint Conference on Neural Networks, IJCNN 2021
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Abstract :

Object proposals generated based on sparse points from the raw point cloud have been widely used in 3D object detection. However, following the above scheme, most existing proposal generators have two problems, one is that the features for proposal generation constrain the detection performance by containing insufficient information; the other is that the sparse points obtained from the raw point cloud are misaligned with their corresponding objects in location and feature aspects. In this paper, we propose a dense-to-sparse proposal generation approach for 3D object detection, which can deal with the two problems simultaneously. Our approach utilizes the 3D CNN backbone to output dense features as a supplement to the original sparse point features for proposal generation. Besides, an object-aware feature pooling module is designed to address the misalignment between sparse points and corresponding objects. Experiments on the KITTI dataset show that our method outperforms the existing sparse-style methods and other published state-of-the-art methods. © 2021 IEEE.

Keyword :

Computer vision Digital signal processing Object detection Object recognition

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GB/T 7714 Yan, Xinrui , Huang, Yuhao , Chen, Shitao et al. DSP-Net: Dense-to-Sparse Proposal Generation Approach for 3D Object Detection on Point Cloud [C] . 2021 .
MLA Yan, Xinrui et al. "DSP-Net: Dense-to-Sparse Proposal Generation Approach for 3D Object Detection on Point Cloud" . (2021) .
APA Yan, Xinrui , Huang, Yuhao , Chen, Shitao , Nan, Zhixiong , Xin, Jingmin , Zheng, Nanning . DSP-Net: Dense-to-Sparse Proposal Generation Approach for 3D Object Detection on Point Cloud . (2021) .
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A joint object detection and semantic segmentation model with cross-attention and inner-attention mechanisms EI SCIE
期刊论文 | 2021 , 463 , 212-225 | NEUROCOMPUTING
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Object detection and semantic segmentation are two fundamental techniques for Intelligent Vehicles (IV) and Advanced Driving Assistance System (ADAS). Motivated by recent studies demonstrating that object detection and semantic segmentation are two highly-correlated tasks, this paper handles the problem of joint object detection and semantic segmentation in traffic scenes. Existing methods perform the joint object detection and semantic segmentation by sharing the same backbone network, but always ignore the interactive connection between the subdividing detection branch and segmentation branch, leading to the insufficient interaction between the two branches. Considering this situation, this paper proposes a joint object detection and semantic segmentation model with the cross-attention and inner-attention mechanisms. The cross-attention mechanism enables to build up the essential interaction between the subdividing detection branch and segmentation branch to fully make use of their correlation. In addition, the inner-attention contributes to strengthening the representations of feature maps in the model. Given an image, an encoder-decoder network is firstly used to extract initial feature maps. Then, the inner-attention mechanism is applied to strengthen the initial feature maps to obtain segmentation feature maps. Subsequently, the cross-attention mechanism utilizes the segmentation feature maps to guide the generation of object detection feature maps. Finally, the semantic segmentation is performed on the segmentation feature maps and object detection is performed on the detection feature maps. In the experiments, two well-known public traffic datasets are used to evaluate our model. Our model achieves the highest performance in comparison with several recently-proposed methods. In addition, some ablation studies are conducted to evaluate the proposed inner-attention and cross-attention mech-anisms, and experiment results validate their effectiveness. (c) 2021 Elsevier B.V. All rights reserved.

Keyword :

Attention Cross-attention Inner-attention Joint detection and segmentation

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GB/T 7714 Nan, Zhixiong , Peng, Jizhi , Jiang, Jingjing et al. A joint object detection and semantic segmentation model with cross-attention and inner-attention mechanisms [J]. | NEUROCOMPUTING , 2021 , 463 : 212-225 .
MLA Nan, Zhixiong et al. "A joint object detection and semantic segmentation model with cross-attention and inner-attention mechanisms" . | NEUROCOMPUTING 463 (2021) : 212-225 .
APA Nan, Zhixiong , Peng, Jizhi , Jiang, Jingjing , Chen, Hui , Yang, Ben , Xin, Jingmin et al. A joint object detection and semantic segmentation model with cross-attention and inner-attention mechanisms . | NEUROCOMPUTING , 2021 , 463 , 212-225 .
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Subspace-based algorithms for localization and tracking of multiple near-field sources EI SCIE Scopus
期刊论文 | 2021 , 6 (3) , 412-426 | IEEE Transactions on Sustainable Computing
WoS CC Cited Count: 18 SCOPUS Cited Count: 3
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Abstract :

In this paper, we investigate the problems of estimating and tracking the location parameters (i.e., directions-of-arrival (DOAs) and ranges] of multiple near-field (NF) narrow-hand sources impinging on a symmetric uniform linear array, and a simple subspace-based algorithm for localization of NF sources (SALONS) is presented, where the computationally burdensome eigen-decomposition and spectrum peak searching are avoided. In the SALONS, the DOAs and ranges are estimated separately with a one-dimensional subspace-based estimation technique, where the null spaces are obtained through the linear operation of the correlation matrices formed from the antidiagonal elements of the noiseless array covariance matrix, and the estimated DOAs and ranges are automatically paired without any additional procedure. Then the statistical analysis of the presented batch SALONS is studied, and the asymptotic mean-squared-error expressions of the estimated DOAs and ranges are derived. Furthermore, an online algorithm is developed for tracking the multiple moving NF sources with crossover points on their trajectories. The effectiveness and the theoretical analysis of the presented algorithms are verified through numerical examples, and the simulation results show that the proposed algorithms provide good estimation and tracking performance for DOAs and show satisfactory estimation and tracking performance for ranges.

Keyword :

Linear operation near-field source localization uniform linear array

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GB/T 7714 Longjun Liu , Hongbin Sun , Chao Li et al. Subspace-based algorithms for localization and tracking of multiple near-field sources [J]. | IEEE Transactions on Sustainable Computing , 2021 , 6 (3) : 412-426 .
MLA Longjun Liu et al. "Subspace-based algorithms for localization and tracking of multiple near-field sources" . | IEEE Transactions on Sustainable Computing 6 . 3 (2021) : 412-426 .
APA Longjun Liu , Hongbin Sun , Chao Li , Tao Li , Jingmin Xin , Nanning Zheng . Subspace-based algorithms for localization and tracking of multiple near-field sources . | IEEE Transactions on Sustainable Computing , 2021 , 6 (3) , 412-426 .
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