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An Automatic Lung Nodule Classification System Based on Hybrid Transfer Learning Approach

SN computer science 2022年第4期3卷 1-10页

作者： Saikia, Trishna Kumar, Ranjeet Kumar, Dilip Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jamshedpur 831014 India School of Electronics Engineering Vellore Institute of Technology Chennai Campus Chennai 600127 India

Lung cancers are not only affecting the lung but also impair the respiratory mechanism. Worldwide, it becomes one of the high causes of mortality in humans. Several computer aided diagnosis (CAD) systems have been designed in recent years for diagnosis of several diseases. Early detection of lung cancer has become very important which can enhance survival chances among humans. The average rates of survival people with lung-cancer can rise from 14 to 49 percent if the diagnosed is taking place in time. Compare to X-ray, computed tomography (CT) is more operative as it involves multiple imaging approaches to support each other. In this study, we have used lung-patient CT scan images to classify the lung nodule into four classes, which are small-cell-carcinoma, adenocarcinoma, squamous-cell-carcinoma and large-cell-carcinoma. This study has proposed VGG networks integrated with support vector machine and random forest to create a hybrid algorithm that further helps in reducing computation complexity of classification. The lung nodules are classified using these hybrids algorithms with highest accuracy 98.70% and compared the results with existing methods. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Computed tomography Convolution neural network Random forest Support vector machine

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FNIN: A Fourier Neural Operator-based Numerical Integration Network for Surface-form-gradients 39

FNIN: A Fourier Neural Operator-based Numerical Integration ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Leng, Jiaqi Ju, Yakun Duan, Yuanxu Zhang, Jiangnan Lv, Qingxuan Wu, Zuxuan Fan, Hao Haide College Ocean University of China China Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China School of Computing and Mathematic Sciences University of Leicester United Kingdom Department of Computer Engineering Qingdao City University China College of Computer Science and Technology Ocean University of China China

ISBN: (纸本)157735897X

Surface-from-gradients (SfG) aims to recover a three-dimensional (3D) surface from its gradients. Traditional methods encounter significant challenges in achieving high accuracy and handling high-resolution inputs, particularly facing the complex nature of discontinuities and the inefficiencies associated with large-scale linear solvers. Although recent advances in deep learning, such as photometric stereo, have enhanced normal estimation accuracy, they do not fully address the intricacies of gradient-based surface reconstruction. To overcome these limitations, we propose a Fourier neural operator-based Numerical Integration Network (FNIN) within a two-stage optimization framework. In the first stage, our approach employs an iterative architecture for numerical integration, harnessing an advanced Fourier neural operator to approximate the solution operator in Fourier space. Additionally, a self-learning attention mechanism is incorporated to effectively detect and handle discontinuities. In the second stage, we refine the surface reconstruction by formulating a weighted least squares problem, addressing the identified discontinuities rationally. Extensive experiments demonstrate that our method achieves significant improvements in both accuracy and efficiency compared to current state-of-the-art solvers. This is particularly evident in handling high-resolution images with complex data, achieving errors of fewer than 0.1 mm on tested objects. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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Robust Reward Placement under Uncertainty

arXiv

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arXiv 2024年

作者： Petsinis, Petros Zhang, Kaichen Pavlogiannis, Andreas Zhou, Jingbo Karras, Panagiotis Department of Computer Science Aarhus University Denmark Artificial Intelligence Thrust Hong Kong University of Science and Technology Guangzhou China Business Intelligence Lab Baidu Research China Department of Computer Science University of Copenhagen Denmark

We consider a problem of placing generators of rewards to be collected by randomly moving agents in a network. In many settings, the precise mobility pattern may be one of several possible, based on parameters outside our control, such as weather conditions. The placement should be robust to this uncertainty, to gain a competent total reward across possible networks. To study such scenarios, we introduce the Robust Reward Placement problem (RRP). Agents move randomly by a Markovian Mobility Model with a predetermined set of locations whose connectivity is chosen adversarially from a known set Π of candidates. We aim to select a set of reward states within a budget that maximizes the minimum ratio, among all candidates in Π, of the collected total reward over the optimal collectable reward under the same candidate. We prove that RRP is NP-hard and inapproximable, and develop Ψ-Saturate, a pseudo-polynomial time algorithm that achieves an ϵ-additive approximation by exceeding the budget constraint by a factor that scales as O(ln |Π|/ϵ). In addition, we present several heuristics, most prominently one inspired by a dynamic programming algorithm for the max-min 0-1 KNAPSACK problem. We corroborate our theoretical analysis with an experimental evaluation on synthetic and real data. © 2024, CC BY.

关键词： Dynamic programming

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SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第2期45卷 2384-2399页

作者： Yang, Xue Yan, Junchi Liao, Wenlong Yang, Xiaokang Tang, Jin He, Tao Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute Shanghai200240 China Anhui University School of Computer Science and Technology Anhui Province Key Laboratory of Multimodal Cognitive Computation Anhui Hefei230093 China Anhui Province Key Laboratory of Multimodal Cognitive Computation Cowarobot Company Ltd. Hefei230601 China

Small and cluttered objects are common in real-world which are challenging for detection. The difficulty is further pronounced when the objects are rotated, as traditional detectors often routinely locate the objects in horizontal bounding box such that the region of interest is contaminated with background or nearby interleaved objects. In this paper, we first innovatively introduce the idea of denoising to object detection. Instance-level denoising on the feature map is performed to enhance the detection to small and cluttered objects. To handle the rotation variation, we also add a novel IoU constant factor to the smooth L1 loss to address the long standing boundary problem, which to our analysis, is mainly caused by the periodicity of angular (PoA) and exchangeability of edges (EoE). By combing these two features, our proposed detector is termed as SCRDet++. Extensive experiments are performed on large aerial images public datasets DOTA, DIOR, UCAS-AOD as well as natural image dataset COCO, scene text dataset ICDAR2015, small traffic light dataset BSTLD and our released S $^{2}$2 TLD by this paper. The results show the effectiveness of our approach. The released dataset S $^{2}$2 TLD is made public available, which contains 5,786 images with 14,130 traffic light instances across five categories. © 1979-2012 IEEE.

关键词： Object detection

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Structural network communication differences in drug-naive depressed adolescents with non-suicidal self-injury and suicide attempts

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World Journal of Psychiatry 2025年第5期15卷 66-78页

作者： Shuai Wang Jiao-Long Qin Lian-Lian Yang Ying-Ying Ji Hai-Xia Huang Xiao-Shan Gao Zi-Mo Zhou Zhen-Ru Guo Ye Wu Lin Tian Huang-Jing Ni Zhen-He Zhou School of Wuxi Medicine Nanjing Medical UniversityWuxi 214000Jiangsu ProvinceChina Department of Clinical Psychology The Affiliated Mental Health Center of Jiangnan UniversityWuxi Central Rehabilitation HospitalWuxi 214000Jiangsu ProvinceChina Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing 210000Jiangsu ProvinceChina School of Medicine Jiangnan UniversityWuxi 214000Jiangsu ProvinceChina Department of Rehabilitation Medicine The Affiliated Mental Health Center of Jiangnan UniversityWuxi Central Rehabilitation HospitalWuxi 214000Jiangsu ProvinceChina Department of Medical Imaging Huadong SanatoriumWuxi 214000Jiangsu ProvinceChina School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210000Jiangsu ProvinceChina Department of Psychiatry The Affiliated Mental Health Center of Jiangnan UniversityWuxi Central Rehabilitation HospitalWuxi 214000Jiangsu ProvinceChina School of Computer Science and Engineering Nanjing University of Posts and TelecommunicationsNanjing 210000Jiangsu ProvinceChina

BACKGROUND Depression,non-suicidal self-injury(NSSI),and suicide attempts(SA)often co-occur during adolescence and are associated with long-term adverse health ***,neural mechanisms underlying self-injury and SA are poorly understood in depressed adolescents but likely relate to the structural abnormalities in brain *** To investigate structural network communication within large-scale brain networks in adolescents with *** We constructed five distinct network communication models to evaluate structural network efficiency at the whole-brain level in adolescents with *** magnetic resonance imaging data were acquired from 32 healthy controls and 85 depressed adolescents,including 17 depressed adolescents without SA or NSSI(major depressive disorder group),27 depressed adolescents with NSSI but no SA(NSSI group),and 41 depressed adolescents with SA and NSSI(NSSI+SA group).RESULTS Significant differences in structural network communication were observed across the four groups,involving spatially widespread brain regions,particularly encompassing cortico-cortical connections(e.g.,dorsal posterior cingulate gyrus and the right ventral posterior cingulate gyrus;connections based on precentral gyrus)and cortico-subcortical circuits(e.g.,the nucleus accumbens-frontal circuit).In addition,we examined whether compromised communication efficiency was linked to clinical symptoms in the depressed *** observed significant correlations between network communication efficiencies and clinical scale scores derived from depressed adolescents with NSSI and *** This study provides evidence of structural network communication differences in depressed adolescents with NSSI and SA,highlighting impaired neuroanatomical communication efficiency as a potential contributor to their *** findings offer new insights into the pathophysiological mechanisms underlying the comorbidity of NSSI and SA in adolescen

关键词： Depression Non-suicidal self-injury Suicide attempts Adolescents Communication models Structural network efficiency

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Guest Editorial: Ubiquitous Intelligence for Internet of Vehicles

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IEEE Internet of Things Magazine 2023年第3期6卷 52-54页

作者： Peng, Haixia Cheng, Nan Ye, Qiang Fang, He Yan, Shen Choo, Kim-Kwang Raymond Duong, Trung Q. California State University Long Beach Department of Computer Engineering and Computer Science Long BeachCA United States School of Telecommunications Engineering Xidian University State Key Lab. of ISN Shaanxi China Memorial University of Newfoundland Department of Computer Science NL Canada Soochow University China Intelligent Automotive Solution BU Huawei Technologies Co. Ltd Policy Standard & Patent Dept. China The University of Texas at San Antonio Cloud Technology Endowed United States Royal Academy of Engineering Queen's University Belfast United Kingdom

As one of the prominent networking paradigms in the realm of the Internet of Things (IoT), the Internet of Vehicles (IoV) facilitates seamless information dissemination and task processing among vehicles equipped with onboard sensing, communication, computing, and storage capabilities. With the advancements in artificial intelligence (AI) techniques, there is a vision to achieve pervasive intelligence within the IoV ecosystem. Various network entities, including connected vehicles, wireless base stations, edge/cloud servers, and aerial/space-assisted devices (such as drones and satellites), are expected to interact efficiently to perceive, reason, and make intelligent decisions based on contextual awareness. These advancements aim to enhance networking and computing effectiveness. The realization of an intelligent, safe, and ubiquitous IoV heavily relies on highly responsive task computing, adaptive networking, and efficient resource control to meet the increasingly diverse requirements of vehicular applications.

关键词： Special issues and sections Internet of Vehicles Intelligent systems Ubiquitous computing

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Few Clean Instances Help Denoising Distant Supervision 29

Few Clean Instances Help Denoising Distant Supervision

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29th International Conference on Computational Linguistics, COLING 2022

作者： Liu, Yufang Huang, Ziyin Wang, Yijun Sun, Changzhi Lan, Man Wu, Yuanbin Mou, Xiaofeng Wang, Ding School of Computer Science and Technology East China Normal University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Bytedance AI Lab China AI Innovation Center Midea Group China

Existing distantly supervised relation extractors usually rely on noisy data for both model training and evaluation, which may lead to garbage-in-garbage-out systems. To alleviate the problem, we study whether a small clean dataset could help improve the quality of distantly supervised models. We show that besides getting a more convincing evaluation of models, a small clean dataset also helps us to build more robust denoising models. Specifically, we propose a new criterion for clean instance selection based on influence functions. It collects sample-level evidence for recognizing good instances (which is more informative than loss-level evidence). We also propose a teacher-student mechanism for controlling purity of intermediate results when bootstrapping the clean set. The whole approach is model-agnostic and demonstrates strong performances on both denoising real (NYT) and synthetic noisy datasets. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Computational linguistics

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Anomaly Detection in Quasi-Periodic Time Series based on Automatic Data Segmentation and Attentional LSTM-CNN (Extended Abstract) 39

Anomaly Detection in Quasi-Periodic Time Series based on Aut...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Liu, Fan Zhou, Xingshe Cao, Jinli Wang, Zhu Wang, Tianben Wang, Hua Zhang, Yanchun Nanjing Research Institute of Electronics Technology Nanjing China School of Computer Science Northwestern Polytechnical University Xi'an China Department of Computer Science and Computer Engineering La Trobe University Melbourne Australia Northwest A&f University Yangling China College of Engineering and Science Victoria University Melbourne Australia Guangzhou University Guangzhou China New Network Dept. Pengcheng Lab Shenzhen China

ISBN: (纸本)9798350322279

Quasi-periodic time series (QTS) exists widely in the real world, and it is important to detect the anomalies of QTS. In this paper, we propose an automatic QTS anomaly detection framework (AQADF) consisting of a two-level clustering-based QTS segmentation algorithm (TCQSA) and a hybrid attentional LSTM-CNN model (HALCM). TCQSA first automatically splits the QTS into quasi-periods which are then classified by HALCM into normal periods or anomalies. Notably, TCQSA integrates a hierarchical clustering and the k-means technique, making itself highly universal and noise-resistant. HALCM hybridizes LSTM and CNN to simultaneously extract the overall variation trends and local features of QTS for modeling its fluctuation pattern. Furthermore, we embed a trend attention gate (TAG) into the LSTM, a feature attention mechanism (FAM) and a location attention mechanism (LAM) into the CNN to finely tune the extracted variation trends and local features according to their true importance to yield a better representation of the fluctuation pattern of the QTS. On four public datasets, HALCM exceeds four state-of-the-art baselines and obtains at least 97.3% accuracy, TCQSA exceeds two cutting-edge QTS segmentation algorithms and can be applied to different types of QTSs. © 2023 IEEE.

关键词： Anomaly detection

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Theoretically Achieving Continuous Representation of Oriented Bounding Boxes

Theoretically Achieving Continuous Representation of Oriente...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zi-Kai Xiao Guo-Ye Yang Xue Yang Tai-Jiang Mu Junchi Yan Shi-Min Hu Department of Computer Science and Technology BNRist Tsinghua University Department of CSE & MoE Key Lab of AI Shanghai Jiao Tong University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Considerable efforts have been devoted to Oriented Ob-ject Detection (OOD). However, one lasting issue regarding the discontinuity in Oriented Bounding Box (OBB) rep-resentation remains unresolved, which is an inherent bot-tleneck for extant OOD methods. This paper endeavors to completely solve this issue in a theoretically guaranteed manner and puts an end to the ad-hoc efforts in this di-rection. Prior studies typically can only address one of the two cases of discontinuity: rotation and aspect ratio, and often inadvertently introduce decoding discontinuity, e.g. Decoding Incompleteness (DI) and Decoding Ambi-guity (DA) as discussed in literature. Specifically, we pro-pose a novel representation method called Continuous OBB (COBB), which can be readily integrated into existing de-tectors e.g. Faster-RCNN as a plugin. It can theoreti-cally ensure continuity in bounding box regression which to our best knowledge, has not been achieved in literature for rectangle-based object representation. For fairness and transparency of experiments, we have developed a modu-larized benchmark based on the open-source deep learning framework Jittor's detection toolbox JDetfor OOD evaluation. On the popular DOTA dataset, by integrating Faster-RCNN as the same baseline model, our new method out-performs the peer method Gliding Vertex by 1.13% mAP 50 (relative improvement 1.54%), and 2.46% mAP 75 (relative improvement 5.91%), without any tricks.

关键词： Deep learning computer vision Computational modeling Detectors Benchmark testing Decoding Sensors

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GeckoDAG: Towards a Lightweight DAG-Based Blockchain via Reducing Data Redundancy

GeckoDAG: Towards a Lightweight DAG-Based Blockchain via Red...

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International Conference on Distributed Computing Systems

作者： Xiaohai Dai Yifan Zhou Jiang Xiao Feng Cheng Xia Xie Hai Jin Bo Li National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China School of Computer Science and Technology Hainan University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

To overcome the scaling and performance limitations, the Directed Acyclic Graph (DAG) is utilized as the underlying storage model of blockchain systems, which enables concurrent transaction processing and confirmation. However, accompanied by high performance, DAG-based blockchains still suffer from the severe challenge of constrained storage scalability, i.e., expensive storage overhead. Based on an in-depth analysis of the data, we discover that the root cause of storage overhead stems from the considerable data redundancy in the DAG-based blockchains. In this paper, we propose GeckoDAG, a lightweight DAG-based blockchain, whose design consists of two steps. First, we abstract a storage model named Basic from the existing DAG-based blockchain systems, which offers both high performance and security. On top of Basic, we then devise GeckoDAG, which merges previous transactions into Transaction Union (TU) and reduces the data redundancy in TU, thus lowering the storage overhead. To evaluate our design, we implement a prototype of GeckoDAG and conduct various experiments on it. The experimental results demonstrate that GeckoDAG can offer storage scalability while maintaining the security and efficiency of DAG-based blockchains.

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