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Lesionmix Data Enhancement and Entropy Minimization for Semi-Supervised Lesion Segmentation of Lung Cancer

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SSRN

SSRN 2024年

作者： Pan, Xipeng Chen, Mingwei Lin, Huan Bian, Xinjun Feng, Siyang Chen, Jiale Wang, Lin Chen, Xin Liu, Zaiyi Lan, Rushi School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin541004 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangdong Guangzhou510080 China State Key Laboratory of Oncology in South China Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Guangdong Provincial Clinical Research Center for Cancer Sun Yat-sen University Cancer Center Guangzhou510060 China Department of Nasopharyngeal Carcinoma Sun Yat-sen University Cancer Center Guangzhou510060 China Department of Radiology Guangzhou First People’s Hospital School of Medicine South China University of Technology Guangdong Guangzhou510180 China Guangdong Provincial People's Hospital China School of Medicine China

Determining the location and contour of the lesion is an important prerequisite for medical diagnosis, subsequent personalized treatment plan and prognostic prediction. Semi-supervised learning and data enhancement methods facilitate deep learning to be used in many fields of medical imaging. In this paper, we propose a data enhancement method named LesionMix, which performs lesion extraction and reuses through a small amount of labeled CT data to improve the utilization efficiency of the labeled data. Meanwhile, we propose a two-stage semi-supervised training strategy called Entropy Minimization LesionMix, in which features containing lesion contour information are quickly learned through LesionMix data augmentation in the first stage. Entropy minimization strategy optimizes the model parameters to alleviate overfitting as much as possible in the second stage and improves prediction confidence. Our proposed method is validated on an public LIDC-IDRI dataset and an in-house GDPHLUAD dataset. Extensive experiments demonstrate that our method achieves promising performance and outperforms seven state-of-the-art semi-supervised models;Moreover, ablation experiments validate the effectiveness of various aspects of our approach. © 2024, The Authors. All rights reserved.

关键词： Diseases

Wireless Electricity Transmission Design of Unmanned Aerial Vehicle Charging Systems 8th

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Wireless Electricity Transmission Design of Unmanned Aerial ...

8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019

作者： He, Yashuo Wu, Jingjing Shi, Sumeng Song, Ze Qiao, Qijing Wang, Cheng Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission Tianjin Normal University Tianjin300387 China Department of Communication Engineering College of Computer and Information Engineering Tianjin Normal University Tianjin300387 China Department of Electronic Science and Technology College of Electronic and Communication Engineering Tianjin Normal University Tianjin300387 China Department of Computer Science and Technology College of Computer and Information Engineering Tianjin Normal University Tianjin300387 China Department of Artificial Intelligence College of Electronic and Communication Engineering Tianjin Normal University Tianjin300387 China

ISBN: (纸本)9789811394089

Unmanned aerial vehicles have been becoming widely used tools along with the developing progresses of robotic, control and energy techniques. As a convenient carrier of payloads and apparatuses for specific missions, the replenishment of battery is significantly important for the using reliability and durability. To overcome the restrictive issue of conventional battery charging dependent on cables, the wireless electricity transmission based on electromagnetic coupling is investigated in this work. The non-contacted charging may enable more powerful facilities of unmanned aerial vehicles, and may further open up new application fields of drone scenarios. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Drones

Multi-AUV Path Planning Based on Hierarchical Opponent Multi-Actor-Attention-Critic Reinforcement Learning Framework

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IEEE Sensors Journal 2025年

作者： Zhu, Yuanbo Han, Guangjie Lin, Chuan Zhang, Fan Hou, Yun Hohai University College of Artificial Intelligence and Automation Changzhou213022 China Ministry of Education Hohai University Key Laboratory of Maritime Intelligent Network Information Technology China Northeastern University Software College Shenyang China Chinese Academy of Sciences State Key Laboratory of Acoustics Institute of Acoustics Beijing100190 China Hohai University College of Information Science and Engineering Changzhou213200 China Hohai University College of Computer Science and Software Engineering Changzhou213022 China

The increasing exploitation of marine resources and intensifying human activities have led to significant marine pollution, highlighting the critical challenge of pollutant dispersal in aquatic environments. To address this challenge, we propose a path-planning framework based on multi-agent reinforcement learning to enable rapid deployment of detection equipment to polluted sites. Specifically, we propose a hierarchical opponent multi-actor-attention-critic framework (HOMAAC) to optimize equipment delivery path planning. In HOMAAC, the hierarchical graphical attention mechanism is employed to enhance cooperative perception and interaction among entities. Opponent modeling is innovatively employed to enhance collaboration efficiency and reduce conflicts among autonomous underwater vehicles (AUVs). This modeling approach enables each AUV to dynamically adjust its motion trajectory, thereby improving the efficiency and safety of equipment delivery and path planning. The results of experiments conducted in a multi-agent underwater logistics delivery environment validate the superior performance of the proposed framework in terms of task completion rate and time spent in addressing the path planning issue, providing valuable insights for the field of intelligent underwater delivery and marine environment monitoring. © 2001-2012 IEEE.

关键词： Marine pollution

Theory of Pressure-Induced Rejuvenation and Strain Hardening in Metallic Glasses

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Physical Review Letters 2021年第2期126卷 025502-025502页

作者： Anh D. Phan Alessio Zaccone Vu D. Lam Katsunori Wakabayashi Faculty of Materials Science and Engineering Computer Science Artificial Intelligence Laboratory Phenikaa Institute for Advanced Study Phenikaa University Hanoi 12116 Vietnam Department of Nanotechnology for Sustainable Energy School of Science and Technology Kwansei Gakuin University Sanda Hyogo 669-1337 Japan Department of Physics “A. Pontremoli” University of Milan via Celoria 16 20133 Milano Italy Cavendish Laboratory University of Cambridge JJ Thomson Avenue CB3 0HE Cambridge United Kingdom Department of Chemical Engineering and Biotechnology Statistical Physics Group University of Cambridge Philippa Fawcett Drive CB3 0AS Cambridge United Kingdom Institute of Materials Science Vietnam Academy of Science and Technology Hanoi 100000 Vietnam Graduate University of Science and Technology Vietnam Academy of Science and Technology Hanoi 100000 Vietnam

We theoretically investigate high-pressure effects on the atomic dynamics of metallic glasses. The theory predicts compression-induced rejuvenation and the resulting strain hardening that have been recently observed in metallic glasses. Structural relaxation under pressure is mainly governed by local cage dynamics. The external pressure restricts the dynamical constraints and slows down the atomic mobility. In addition, the compression induces a rejuvenated metastable state (local minimum) at a higher energy in the free-energy landscape. Thus, compressed metallic glasses can rejuvenate and the corresponding relaxation is reversible. This behavior leads to strain hardening in mechanical deformation experiments. Theoretical predictions agree well with experiments.

关键词： Plastic deformation Pressure effects Strain Glassy systems Metallic glasses Metals

Unsupervised embedding learning from uncertainty momentum modeling

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arXiv 2021年

作者： Zhou, Jiahuan Tang, Yansong Su, Bing Wu, Ying Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Department of Engineering Science University of Oxford OX1 3PJ United Kingdom Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China

Existing popular unsupervised embedding learning methods focus on enhancing the instance-level local discrimination of the given unlabeled images by exploring various negative data. However, the existed sample outliers which exhibit large intra-class divergences or small inter-class variations severely limit their learning performance. We justify that the performance limitation is caused by the gradient vanishing on these sample outliers. Moreover, the shortage of positive data and disregard for global discrimination consideration also pose critical issues for unsupervised learning but are always ignored by existing methods. To handle these issues, we propose a novel solution to explicitly model and directly explore the uncertainty of the given unlabeled learning samples. Instead of learning a deterministic feature point for each sample in the embedding space, we propose to represent a sample by a stochastic Gaussian with the mean vector depicting its space localization and covariance vector representing the sample uncertainty. We leverage such uncertainty modeling as momentum to the learning which is helpful to tackle the outliers. Furthermore, abundant positive candidates can be readily drawn from the learned instance-specific distributions which are further adopted to mitigate the aforementioned issues. Thorough rationale analyses and extensive experiments are presented to verify our superiority. © 2021, CC BY.

关键词： Embeddings

Deep Learning Techniques for Automatic Lateral X-ray Cephalometric Landmark Detection: Is the Problem Solved?

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arXiv 2024年

作者： Zhang, Hongyuan Wang, Ching-Wei Muzakky, Hikam Dai, Juan Li, Xuguang Ma, Chenglong Wu, Qian Cui, Xianan Xu, Kunlun He, Pengfei Guo, Dongqian Wang, Xianlong Lee, Hyunseok Zhong, Zhangnan Zhu, Zhu Huang, Bingsheng Medical AI Lab School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen518060 China Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taiwan Department of Stomatology Shenzhen University General Hospital Shenzhen University Shenzhen518055 China Institute of Stomatological Research Shenzhen University Shenzhen518055 China Chohotech Technology Co. Ltd. Lianchuang Street Yuhang District Hangzhou311422 China Singapore School of Computer Science Wuhan University Wuhan430072 China Wangxuan Institute of Computer Technology Peking University Beijing100871 China School of Artificial Intelligence Xidian University Xi’an710126 China State Key Laboratory of Internet of Things for Smart City University of Macau Taipa China The Faculty of Information Science and Engineering Ocean University of China Qingdao266100 China Digital Healthcare Department Daegu-Gyeongbuk Medical Innovation Foundation Daegu427724 Korea Republic of School of Life Science South China Normal University Guangzhou510631 China Information and Data Department Children’s Hospital Zhejiang University School of Medicine Hangzhou310000 China

Localization of the craniofacial landmarks from lateral cephalograms is a fundamental task in cephalometric analysis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Cephalometric Landmark Detection (CL-Detection)" dataset, which is the largest publicly available and comprehensive dataset for cephalometric landmark detection. This multi-center and multi-vendor dataset includes 600 lateral X-ray images with 38 landmarks acquired with different equipment from three medical centers. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go for cephalometric landmark detection. Following the 2023 MICCAI CL-Detection Challenge, we report the results of the top ten research groups using deep learning methods. Results show that the best methods closely approximate the expert analysis, achieving a mean detection rate of 75.719% and a mean radial error of 1.518 mm. While there is room for improvement, these findings undeniably open the door to highly accurate and fully automatic location of craniofacial landmarks. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for the community to benchmark future algorithm developments at https: //***/. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

Adaptive feature selection with weighted fuzzy rough sets for noisy data

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Fuzzy Sets and Systems 2025年 518卷

作者： Xiaoling Yang Hongmei Chen Tianrui Li Shan Feng Jihong Wan Yiyu Yao School of Mathematical Sciences Sichuan Normal University China School of Computing and Artificial Intelligence Southwest Jiaotong University China Department of Computer Science University of Regina Regina Canada National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University China

The existence of noise in data may decrease the performance of the classification model. An intuitive assumption suggests that samples with a higher local density have a greater probability of being correctly classified. Moreover, there are differences in the importance of features. It should further increase the influence of important features on the classification task. Motivated by these issues, we propose a sample-driven adaptive feature selection method. It aims to the impact of noise and obtain the adaptive importance of features. In this paper, the Weighted Fuzzy Rough Sets model (WFRS) is proposed by utilizing more reliable samples to address noisy data. Firstly, the approximation margin is derived by applying the WFRS model within the original feature space from the sample's perspective. It serves as a basis for deriving the feature weights. On this basis, the weighted feature space is obtained. This space compresses the features with the lower weight and stretches the features with higher weight. Then, we obtain a specific fuzzy relation to maximize the within-class samples and minimize the between-class samples in the weighted feature space. Further, an evaluation function of features is constructed to model the uncertainty of fuzzy positive and non-negative regions. A Sample-driven adaptive Feature selection algorithm based on the Weighted fuzzy rough sets model (SFW) is designed to capture the varying importance of features by considering the local density of samples. The weighted features are subsequently selected to facilitate the downstream classification tasks. Experimental results show the WFRS model's robustness, the effectiveness of SFW, and its superiority compared to alternative methods.

关键词：

GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray Images

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arXiv 2021年

作者： Qi, Baolian Zhao, Gangming Wei, Xin Du, Changde Pan, Chengwei Yu, Yizhou Li, Jinpeng HwaMei Hospital University of Chinese Academy of Sciences NO.41 Northwest Street Haishu District Zhejiang Ningbo315010 China Department of Computer Science The University of Hong Kong Hong Kong National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Institute of Artificial Intelligence Beihang University Beijing China

Locating diseases in chest X-ray images with few careful annotations saves large human effort. Recent works approached this task with innovative weakly-supervised algorithms such as multi-instance learning (MIL) and class activation maps (CAM), however, these methods often yield inaccurate or incomplete regions. One of the reasons is the neglection of the pathological implications hidden in the relationship across anatomical regions within each image and the relationship across images. In this paper, we argue that the cross-region and cross-image relationship, as contextual and compensating information, is vital to obtain more consistent and integral regions. To model the relationship, we propose the Graph Regularized Embedding Network (GREN), which leverages the intra-image and inter-image information to locate diseases on chest X-ray images. GREN uses a pre-trained U-Net to segment the lung lobes, and then models the intra-image relationship between the lung lobes using an intra-image graph to compare different regions. Meanwhile, the relationship between in-batch images is modeled by an inter-image graph to compare multiple images. This process mimics the training and decision-making process of a radiologist: comparing multiple regions and images for diagnosis. In order for the deep embedding layers of the neural network to retain structural information (important in the localization task), we use the Hash coding and Hamming distance to compute the graphs, which are used as regularizers to facilitate training. By means of this, our approach achieves the state-of-the-art result on NIH chest X-ray dataset for weakly-supervised disease localization. © 2021, CC BY.

关键词： Hamming distance

10-Mega pixel snapshot compressive imaging with a hybrid coded aperture

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arXiv 2021年

作者： Zhang, Zhihong Deng, Chao Liu, Yang Yuan, Xin Suo, Jinli Dai, Qionghai Department of Automation Tsinghua University Beijing100084 China Institute for Brain and Cognitive Science Tsinghua University Beijing100084 China Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Bell Labs 600 Mountain Avenue Murray HillNJ07974 United States Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

High resolution images are widely used in our daily life, whereas high-speed video capture is challenging due to the low frame rate of cameras working at the high resolution mode. Digging deeper, the main bottleneck lies in the low throughput of existing imaging systems. Towards this end, snapshot compressive imaging (SCI) was proposed as a promising solution to improve the throughput of imaging systems by compressive sampling and computational reconstruction. During acquisition, multiple high-speed images are encoded and collapsed to a single measurement. After this, algorithms are employed to retrieve the video frames from the coded snapshot. Recently developed Plug-and-Play (PnP) algorithms make it possible for SCI reconstruction in large-scale problems. However, the lack of high-resolution encoding systems still precludes SCI's wide application. In this paper, we build a novel hybrid coded aperture snapshot compressive imaging (HCA-SCI) system by incorporating a dynamic liquid crystal on silicon and a high-resolution lithography mask. We further implement a PnP reconstruction algorithm with cascaded denoisers for high quality reconstruction. Based on the proposed HCA-SCI system and algorithm, we achieve a 10-mega pixel SCI system to capture high-speed scenes, leading to a high throughput of 4.6G voxels per second. Both simulation and real data experiments verify the feasibility and performance of our proposed HCA-SCI scheme. Copyright © 2021, The Authors. All rights reserved.

关键词： Imaging systems