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Learning feature fusion via an interpretation method for tumor segmentation on PET/CT

APPLIED SOFT COMPUTING 2023年 148卷

作者： Kang, Susu Chen, Zhiyuan Li, Laquan Lu, Wei Qi, X. Sharon Tan, Shan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Minist Educ China 1037 Luo Yu Rd Wuhan 430074 Hubei Peoples R China Chongqing Univ Posts & Telecommun Sch Sci 2 Chong Wen Rd Chongqing 400065 Peoples R China Mem Sloan Kettering Canc Ctr Dept Med Phys 1275 York Ave New York NY 10065 USA Univ Calif Los Angeles Sch Med Dept Radiat Oncol 10833 Le Conte Ave Los Angeles CA 90001 USA Huazhong Univ Sci & Technol Innovat Inst 1037 Luo Yu Rd Wuhan 430074 Hubei Peoples R China

Accurate tumor segmentation of multi-modality PET/CT images plays a vital role in computer-aided cancer diagnosis and treatment. It is crucial to rationally fuse the complementary information in multi-modality PET/CT segmentation. However, existing methods usually lack interpretability and fail to sufficiently identify and aggregate critical information from different modalities. In this study, we proposed a novel segmentation framework that incorporated an interpretation module into the multi-modality segmentation backbone. The interpretation module highlighted critical features from each modality based on their contributions to the segmentation performance. To provide explicit supervision for the interpretation module, we introduced a novel interpretation loss with two fusion schemes: strengthened fusion and perturbed fusion. The interpretation loss guided the interpretation module to focus on informative features, enhancing its effectiveness in generating meaningful interpretable masks. Under the guidance of the interpretation module, the proposed approach can fully exploit meaningful features from each modality, leading to better integration of multi-modality information and improved segmentation performance. Ablative and comparative experiments were conducted on two PET/CT tumor segmentation datasets. The proposed approach surpassed the baseline by 1.4 and 1.8 Dices on two datasets, respectively, indicating the improvement achieved by the interpretation method. Furthermore, the proposed approach outperformed the best comparison approach by 0.9 and 0.6 Dices on two datasets, respectively. In addition, visualization and perturbation experiments further illustrated the effectiveness of the interpretation method in highlighting critical features.

关键词： Tumor segmentation PET/CT images Feature fusion Network interpretation Deep learning

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Joint Observer Gain and Input Design for Asymptotic Active Fault Diagnosis

arXiv

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arXiv 2024年

作者： Xu, Feng Wan, Yiming Wang, Ye Puig, Vicenç Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education China Wuhan430074 China Department of Electrical and Electronic Engineering The University of Melbourne ParkvilleVIC3010 Australia Universitat Politècnica de Catalunya Barcelona08028 Spain

This paper proposes a joint gain and input design method for observer-based asymptotic active fault diagnosis, which is based on a newly-defined notion named the excluding degree of the origin from a zonotope. Using the excluding degree, a quantitative specification is obtained to characterize the performance of set-based robust fault diagnosis. Furthermore, a single gain design method and a joint gain and input design method are proposed, respectively. This is the first work to achieve a joint observer gain and input design for set-based active fault diagnosis. Compared with the existing methods that design gains and input separately, the proposed joint gain and input design method has advantages to exploit the fault diagnosis potential of observer-based schemes. Finally, several examples are used to illustrate the effectiveness of the proposed methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Design

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Balancing control of neutral-point voltage for three-level T-type inverter based on hybrid variable virtual space vector

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IET POWER ELECTRONICS 2020年第4期13卷 744-750页

作者： Zhou, Jiancheng Cheng, Shanmei Hu, Zhenyu Liu, Jiang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan Peoples R China

A three-level T-type inverter has higher efficiency and lower output voltage harmonics compared with the traditional two-level inverter. However, neutral-point voltage fluctuation and common-mode voltage (CMV) can negatively affect the performance of the three-level T-type inverter. This study proposes a novel hybrid variable virtual space vector ((HVSV)-S-2) strategy to mitigate this problem. Based on the virtual space vector scheme, new variable virtual small and medium vectors are reconstructed with the principle that the change of neutral-point charge of each virtual vector in a switching period is zero, and the adjustment factor is further added to the virtual vector to achieve the balance of the neutral-point voltage. At the same time, the vectors with a large CMV are eliminated to ensure that the CMV is minimised. Therefore, the (HVSV)-S-2 has excellent neutral-point voltage balance control capability and common-voltage suppression at both high- and low-modulation ratios. Theoretical analysis, simulations, and experimental results demonstrate the effectiveness and feasibility of the proposed strategy for full modulation ratios.

关键词： switching convertors PWM invertors voltage control two-level inverter neutral-point voltage fluctuation common-mode voltage CMV three-level T-type inverter virtual space vector scheme variable virtual small vectors medium vectors neutral-point charge virtual vector common-voltage suppression lower output voltage harmonics neutral-point voltage balance control capability hybrid variable virtual space vector

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On the equivalence between the unbiased minimum-variance estimation and the infinity augmented Kalman filter

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INTERNATIONAL JOURNAL OF control 2020年第12期93卷 2995-3002页

作者： Ding, Bo Zhang, Tianping Fang, Huajing Yangzhou Univ Coll Informat Engn Dept Automat Yangzhou Jiangsu Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Sch Automat Wuhan Peoples R China

In this paper, the augment Kalman filter (AKF) approach is discussed under the infinity condition. It is proved that the AKF in infinity condition is equivalent to the minimum-variance unbiased (MVU) estimation proposed in Gillijns & De Moor [(2007). Unbiased minimum-variance input and state estimation for linear discrete-time systems. Automatica, 43(1), 111-116]. This result can be regarded as a new contribution in the field of joint input and state estimation. Meanwhile, this result gives another way to estimate the state and unknown input simultaneously. Finally, the theoretical result is validated by simulations.

关键词： Infinity augment Kalman filter state estimation unknown input estimation linear discrete-time system

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Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2020年第2期67卷 399-410页

作者： He, He Wu, Dongrui Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Objective: This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data? Methods: We propose a novel approach to align EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: first, it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction, and machine learning algorithms can then be applied to the aligned trials;second, its computational cost is very low;and third, it is unsupervised and does not need any label information from the new subject. Results: Both offline and simulated online experiments on motor imagery classification and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment. Conclusion: The proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs. Significance: Our proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs.

关键词： Covariance matrices Electroencephalography Task analysis Signal processing Machine learning algorithms Microsoft Windows Brain-computer interfaces Brain-computer interface data alignment EEG Riemannian geometry transfer learning

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Exploiting Distilled Learning for Deep Siamese Tracking 25

Exploiting Distilled Learning for Deep Siamese Tracking

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25th International Conference on Pattern Recognition (ICPR)

作者： Liu, Chengxin Cao, Zhiguo Li, Wei Xiao, Yang Du, Shuaiyuan Zhu, Angfan Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Queen Mary Univ London London England

ISBN: (纸本)9781728188089

Existing deep siamese trackers are typically built on off-the-shelf CNN models for feature learning, with the demand for huge power consumption and memory storage. This limits current deep siamese trackers to be carried on resource-constrained devices like mobile phones, given factor that such a deployment normally requires cost-effective considerations. In this work, we address this issue by presenting a novel Distilled Learning Framework(DLF) for siamese tracking, which aims at learning tracking model with efficiency and high accuracy. Specifically, we propose two simple yet effective knowledge distillation strategies, denote as point-wise distillation and pair-wise distillation, which are designed for transferring knowledge from a more discriminative teacher tracker into a compact student tracker. In this way, cost-effective and high performance tracking could be achieved. Extensive experiments on several tracking benchmarks demonstrate the effectiveness of our proposed method.

关键词： Distillation

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Basic theorem and global exponential stability of differential-algebraic neural networks with delay

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NEURAL NETWORKS 2021年 140卷 336-343页

作者： Chen, Jiejie Chen, Boshan Zeng, Zhigang Hubei Normal Univ Coll Comp Sci & Informat Engn Huangshi 435002 Hubei Peoples R China Hubei Normal Univ Coll Math & Stat Huangshi 435002 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

A differential-algebraic neural network (DANN) with delay (DDANN) is proposed. Firstly, the global existence and uniqueness theorems are established for a DDANN, respectively. Next, a new differential-algebraic inequality is established. Then, a theorem on global exponential stability of DDANN is shown by using this inequality. As an application of DDANN, a very concise criterion on global exponential stability for a neutral-type neural network is given by using DDANNs. Finally, two examples are given to illustrate the theoretical results. (C) 2021 Published by Elsevier Ltd.

关键词： Global existence and uniqueness theorem Global exponential stability Differential-algebraic neural networks Singular neural networks Neutral-type neural networks

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Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians 16th

Do Not Disturb Me: Person Re-identification Under the Interf...

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16th European Conference on Computer Vision, ECCV 2020

作者： Zhao, Shizhen Gao, Changxin Zhang, Jun Cheng, Hao Han, Chuchu Jiang, Xinyang Guo, Xiaowei Zheng, Wei-Shi Sang, Nong Sun, Xing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent Youtu Lab Shanghai China Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783030585389

In the conventional person Re-ID setting, it is assumed that cropped images are the person images within the bounding box for each individual. However, in a crowded scene, off-shelf-detectors may generate bounding boxes involving multiple people, where the large proportion of background pedestrians or human occlusion exists. The representation extracted from such cropped images, which contain both the target and the interference pedestrians, might include distractive information. This will lead to wrong retrieval results. To address this problem, this paper presents a novel deep network termed Pedestrian-Interference Suppression Network (PISNet). PISNet leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query. Furthermore, the involving Guidance Reversed Attention Module and the Multi-Person Separation Loss promote QGAB to suppress the interference of other pedestrians. Our method is evaluated on two new pedestrian-interference datasets and the results show that the proposed method performs favorably against existing Re-ID methods. © 2020, Springer Nature Switzerland AG.

关键词： Computers

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Exponential quasi-synchronization of coupled delayed memristive neural networks via intermittent event-triggered control

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NEURAL NETWORKS 2021年 141卷 98-106页

Firstly, an intermittent event-triggered control (IETC), as a combination of intermittent control and event-triggered control, is proposed. Then, the quasi-synchronization problem of coupled memristive neural networks with time-varying delays (CDMNN) is discussed under this IETC. To include more of the existing work, aperiodic intermittent control and event-triggered control with combined measurement errors are adopted in the IETC. Under the IETC, it is shown that Zeno behavior cannot be exhibited for CDMNN. At the same time, two new differential inequalities are established, and some simple and practical criteria for CDMNN quasi-synchronization and synchronization are obtained by using these inequalities. In the obtained results, synchronization is a spatial case of quasi-synchronization, and the activation functions of DMNN do not need to be bounded. Finally, a numerical example and some simulations are provided to test the results in theoretical analysis. (C) 2021 Published by Elsevier Ltd.

关键词： Coupled memristive neural networks Intermittent event-triggered control Exponential synchronization Quasi-synchronization

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Semi-Supervised Low-Rank Semantics Grouping for Zero-Shot Learning

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IEEE TRANSACTIONS ON image processing 2021年 30卷 2207-2219页

作者： Xu, Bingrong Zeng, Zhigang Lian, Cheng Ding, Zhengming Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China Tulane Univ Dept Comp Sci New Orleans LA 70118 USA

Zero-shot learning has received great interest in visual recognition community. It aims to classify new unobserved classes based on the model learned from observed classes. Most zero-shot learning methods require pre-provided semantic attributes as the mid-level information to discover the intrinsic relationship between observed and unobserved categories. However, it is impractical to annotate the enriched label information of the observed objects in real-world applications, which would extremely hurt the performance of zero-shot learning with limited labeled seen data. To overcome this obstacle, we develop a Low-rank Semantics Grouping (LSG) method for zero-shot learning in a semi-supervised fashion, which attempts to jointly uncover the intrinsic relationship across visual and semantic information and recover the missing label information from seen classes. Specifically, the visual-semantic encoder is utilized as projection model, low-rank semantic grouping scheme is explored to capture the intrinsic attributes correlations and a Laplacian graph is constructed from the visual features to guide the label propagation from labeled instances to unlabeled ones. Experiments have been conducted on several standard zero-shot learning benchmarks, which demonstrate the efficiency of the proposed method by comparing with state-of-the-art methods. Our model is robust to different levels of missing label settings. Also visualized results prove that the LSG can distinguish the test unseen classes more discriminative.

关键词： Semantics Visualization Training Learning systems Correlation Task analysis Laplace equations Zero-shot learning semi-supervised low-rank semantic grouping label propagation

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