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IEEE International Symposium on Biomedical Imaging

作者： Shijie Pang Yuze Zhao Rui-Wei Zhao Rui Feng Academy for Engineering and Technology Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

In real clinical scenarios, diagnosis algorithms may face the complex concurrent presence of multiple common, rare, or even unknown disease conditions. In particular, rare disease categories may be underrepresented or even absent from the class-imbalanced training set, leaving safe and trustworthy diagnostic decision-making extremely challenging. In this work, we first formalize the medical image diagnosis as a multi-label open set recognition under class-imbalanced training data and propose a novel Multi-label Open set Diagnosis (MOSD) solution to address this problem. Our MOSD first employs a Balanced Multi-label Data Augmentation (BMDA) module to augment multi-label image data to alleviate the class imbalance in model training more effectively. Then it also boasts a Multi-label Evidential Deep Learning (MEDL) module with tailored learning objectives to better estimate the uncertainty for multi-label open set prediction. Experimental results on the ODIR-5K benchmark successfully demonstrate its superiority in both close set known class classification and open set unknown class detection.

关键词： Training Uncertainty Image recognition Face recognition Training data Multi label classification Robustness Medical diagnosis Medical diagnostic imaging Diseases

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Rockburst prediction using artificial intelligence techniques:A review

Rock Mechanics Bulletin

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Rock Mechanics Bulletin 2024年第3期3卷 1-13页

作者： Yu Zhang Kongyi Fang Manchao He Dongqiao Liu Junchao Wang Zhengjia Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China Thomas Lord Department of Computer Science University of Southern CaliforniaLos Angeles90007CAUSA

Rockburst is a phenomenon where sudden,catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation *** disasters endanger the safety of people's lives and property,national energy security,and social interests,so it is very important to accurately predict *** rockburst prediction has not been able to find an effective prediction method,and the study of the rockburst mechanism is facing a *** the development of artificial intelligence(AI)techniques in recent years,more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst *** previous research,several scholars have attempted to summarize the application of AI techniques in rockburst ***,these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction,or they do not provide a comprehensive *** on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques,this paper conducts a comprehensive review of rockburst prediction methods leveraging AI ***,pertinent definitions of rockburst and its associated hazards are ***,the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized,with emphasis placed on the respective advantages and disadvantages of each ***,the strengths and weaknesses of prediction methods leveraging AI are summarized,alongside forecasting future research trends to address existing challenges,while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

关键词： Rockburst Rockburst prediction Artificial intelligence techniques

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Disentangle Nighttime Lens Flares: Self-supervised Generation-based Lens Flare Removal 39

Disentangle Nighttime Lens Flares: Self-supervised Generatio...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： He, Yuwen Wang, Wei Wu, Wanyu Jiang, Kui Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China School of Computer Science and Technology Harbin Institute of Technology China

ISBN: (纸本)157735897X

Lens flares arise from light reflection and refraction within sensor arrays, whose diverse types include glow, veiling glare, reflective flare and so on. Existing methods are specialized for one specific type only, and overlook the simultaneous occurrence of multiple typed lens flares, which is common in the real-world, e.g. coexistence of glow and displacement reflections from the same light source. These co-occurring lens flares cannot be effectively resolved by the simple combination of individual flare removal methods, since these coexisting flares originates from the same light source and are generated simultaneously within the same sensor array, exhibit a complex interdependence rather than simple additive relation. To model this interdependent flares’ relationship, our Nighttime Lens Flare Formation model is the first attempt to learn the intrinsic physical relationship between flares on the imaging plane. Building on this physical model, we introduce a solution to this joint flare removal task named Self-supervised Generation-based Lens Flare Removal Network (SGLFR-Net), which is self-supervised without pre-training. Specifically, the nighttime glow is detangled in PSF Rendering Network(PSFR-Net) based on PSF Rendering Prior, while the reflective flare is modelled in Texture Prior Based Reflection Flare Removal Network (TPRR-Net). Empirical evaluations demonstrate the effectiveness of the proposed method in both joint and individual glare removal tasks. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Luminescent devices

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The Methodology of Mask Detection Employing the YOLOv7 Framework

The Methodology of Mask Detection Employing the YOLOv7 Frame...

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Neural Networks, information and Communication Engineering (NNICE), International Conference on

作者： Fucheng Zhang Pinfang Tian School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real-TimeIndustrial Systems Wuhan China

In the current field of object detection, the YOLOv7 model demonstrates significant advantages in terms of both detection speed and accuracy. However, the model shows deficiencies in focussing on key information, and its loss function's sensitivity to bounding boxes results in suboptimal performance in densely populated environments and small target detection scenarios. To address these issues, this study proposes a mask target detection method based on YOLOv7. By integrating a channel attention mechanism into the YOLOv7 model, this method enhances the model's focus on important information in images, effectively reducing interference from background factors. Additionally, the method incorporates Normalized Wasserstein Distance (NWD) into the loss function to optimize the model's evaluation of small target detection. Experimental verification shows that the improved model maintains its original detection speed while increasing the overall accuracy to 92.1%, a 2.9% improvement over the original YOLOv7 model. It achieves a 3.3% increase in the mAP.5 metric. This indicates that the optimised YOLOv7 model has superior detection capabilities, allowing it to perform mask-wearing detection tasks more rapidly and accurately in natural environments.

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EDGE: Unknown-aware Multi-label Learning by Energy Distribution Gap Expansion

arXiv

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arXiv 2024年

作者： Sun, Yuchen Xu, Qianqian Wang, Zitai Yang, Zhiyong He, Junwei Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Multi-label Out-Of-Distribution (OOD) detection aims to discriminate the OOD samples from the multi-label In-Distribution (ID) ones. Compared with its multiclass counterpart, it is crucial to model the joint information among classes. To this end, JointEnergy, which is a representative multi-label OOD inference criterion, summarizes the logits of all the classes. However, we find that JointEnergy can produce an imbalance problem in OOD detection, especially when the model lacks enough discrimination ability. Specifically, we find that the samples only related to minority classes tend to be classified as OOD samples due to the ambiguous energy decision boundary. Besides, imbalanced multi-label learning methods, originally designed for ID ones, would not be suitable for OOD detection scenarios, even producing a serious negative transfer effect. In this paper, we resort to auxiliary outlier exposure (OE) and propose an unknown-aware multi-label learning framework to reshape the uncertainty energy space layout. In this framework, the energy score is separately optimized for tail ID samples and unknown samples, and the energy distribution gap between them is expanded, such that the tail ID samples can have a significantly larger energy score than the OOD ones. What’s more, a simple yet effective measure is designed to select more informative OE datasets. Finally, comprehensive experimental results on multiple multi-label and OOD datasets reveal the effectiveness of the proposed method. © 2024, CC BY.

关键词： Contrastive Learning

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FaceGCN: Structured Priors Inspired Graph Convolutional Networks for Blind Face Restoration

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Yan, Weidan Shao, Wenze Zhang, Dengyin Xiao, Liang Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing210003 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing210003 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Facial image restoration has gained a tremendous progress since the increasing boom of the deep learning methods. Owing to its nature of strong ill-posedness, different categories of a-priori constraints have been harnessed or embedded in the existing deep architectures. While, as it turns to blind face restoration with more complicated degradations, the challenge becomes greater. In this paper, a further insightful step is taken by exploring the potentials of the graph convolutional networks (GCN) in conjunction with the structured priors for the blind problem. Specifically, a lightweight yet physically more intuitive model termed FaceGCN is proposed. On the one hand, a dynamic generator of facial adjacency matrices is constructed assisted by two self-supervised losses, allowing a sparse, accurate, and adaptive construction of case-specific face graphs with facial feature components as nodes. On the other hand, to model well the joint local-nonlocal correlations among various facial feature components, a kind of novel strip-attention GCN modules is correspondingly developed by splitting facial feature maps into intra- and inter-strips in both horizontal and vertical orientations, respectively. Extensive experimental results show that FaceGCN has achieved comparable or even superior performance to state-of-the-art methods, yet at a considerably less computational cost. © 1991-2012 IEEE.

关键词： Convolution

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Adaptive Image Steganographic Analysis System Based on Deep Convolutional Neural Network 11th

Adaptive Image Steganographic Analysis System Based on Deep ...

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11th International Conference on computer Engineering and Networks, CENet2021

作者： Jiao, Ge College of Computer Science and Technology Hengyang Normal University HengyangHunan421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application HengyangHunan421002 China

ISBN: (纸本)9789811665530

Adaptive steganography emplaces the message into the hard-to-detect noise area or the complex texture area of the image, so the steganography analysis method based on artificial design features needs to design a very complex feature extraction algorithm to detect the steganography image. In view of the advantages of deep learning steganography in automatic extraction of image features and high detection accuracy, an image steganography analysis algorithm is designed by using deep convolutional neural network, and an image steganography analysis system based on deep learning is developed. The system realizes the functions of neural network structure analysis, image steganography and verification, steganography analysis and so on. The system simplifies the operation process of algorithm comparison, reduces the complexity of algorithm performance evaluation, and verifies the feasibility of the proposed algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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MSCAP: DNA Methylation Age Predictor based on Multiscale Convolutional Neural Network

MSCAP: DNA Methylation Age Predictor based on Multiscale Con...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Han Cai, Ruirui Zong, Xizeng He, Zhiquan Zhang, Li Northeast Normal University School of Information Science and Technology Institute of Computational Biology Changchun China South China University of Technology Clinical Research Centre Guangzhou First People's Hospital School of Medicine Guangzhou China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Changchun University of Technology School of Computer Science and Engineering Changchun China

ISBN: (纸本)9798350337488

DNA methylation can reflect age-related issues in individuals, and age prediction tools developed based on DNA methylation are called Epigenetic clocks. Epigenetic clocks also serve as potent tools for enhancing researchers' understanding of the aging process and aim to contribute to the advancement of aging research. The current prevailing approach involves creating clocks using Elastic Net regression modeling, wherein high-age-correlation CpG sites were selected as features in advance. However, pre-selecting CpG sites in advance may inadvertently overlook interactions between distant CpG sites. Deep learning has significant advantages in dealing with vast feature sets due to the tremendous opportunities that the development of deep learning presents for research. Deep learning architectures have demonstrated greater efficiency in acquiring and representing large numbers of intricate features compared to traditional machine learning models. In this study, we proposed a deep learning model based on a multiscale convolutional neural network (MSCNN) for pan-tissue age prediction, called MSCAP. First, for the model to efficiently predict datasets from different sequencing platforms, we chose a common intersection of sites from the Illumina 27K and Illumina 450K platforms (25,789 CpG sites), then trained and tested the model on 93 datasets. Different tissues of the human body are used in the datasets for training and testing. And independently tested on 7 independent datasets against an Epigenetic clock developed by Horvath in 2013 with high prediction accuracy. The results showed that MSCAP could effectively extract key features from a vast number of potential features without advanced feature selection. In addition, MSCAP outperformed the Horvath clock on all seven independent test datasets. This research helped boost the development of epigenetic clock methods and reinforced the value of deep learning in computational biology. © 2023 IEEE.

关键词： age prediction DNA methylation epigenetic clock multiscale convolutional neural network (MSCNN)

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ViT-MGI: Context-aware Lightweight Malicious Gradient Identification for Federated Vision Transformer Systems against Poisoning Attacks 10

ViT-MGI: Context-aware Lightweight Malicious Gradient Identi...

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10th IEEE Smart World Congress, SWC 2024

作者： Yang, Shujie Li, Tengfei Zhou, Zan Hao, Hao Xu, Tianyu Wang, Bo Guan, Jianfeng Zhou, Tianhang Yao, Su Beijing University of Posts and Telecommunications School of Computer Science Beijing China Shandong Academy of Sciences & National Supercomputing Center Jinan China College of Carbon Neutrality Future Technology State Key Laboratory of Heavy Oil Processing Beijing102249 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing China

ISBN: (纸本)9798331520861

With the continuous development of large vision models, training requires increasingly large datasets. Federated Learning for Vision Transformer models (Federated ViT) is needed to train models without data leaving multiple clients while capturing complex global features. However, various attacks, such as tampering with gradient data, label-flipping, and backdoor attacks, pose significant challenges to federated learning. Existing defense mechanisms like the Krum algorithm, PCA, and Sniper scheme exhibit low efficiency and poor robustness in Federated ViT scenarios due to the vast number of parameters and inactive neurons. This paper proposes a two-stage, context-aware lightweight malicious gradient identification method for ViT to improve the efficiency and robustness of malicious user detection. By using feature layer extraction and Principal Component Analysis (PCA), we eliminate invalid gradient information, enhancing both detection efficiency and robustness. We reduced data dimensions to 0. 4 % of the original without compromising detection accuracy. The Isolation Forest algorithm distinguishes between malicious and benign users based on extracted gradient features, and a Subjective Logic Model performs temporal accumulation of user scores to improve accuracy and reduce erroneous identifications. Experiments on CIFAR-10, MNIST, and OrganAMNIST datasets show that our model reduces processing time by approximately 70% compared to advanced PCA methods, while significantly improving accuracy and F1 scores. Additionally, our method offers substantial advantages in both efficiency and robustness compared to pooling techniques aimed at reducing computational load. We have named our model ViT-MGI and released its source code to facilitate future research in this field: https://***/LetMeFly666/ViT-MGI. © 2024 IEEE.

关键词： Federated learning

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Hybrid Spatial-Frequency Attention Network For Fine-Grained Skeleton-Based Action Recognition

Hybrid Spatial-Frequency Attention Network For Fine-Grained ...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Dandan Zhang Sicong Zhan Jia Wang Zhiqing Guo School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Multilingual Information Technology Urumqi China Xinjiang Multimodal Intelligent Processing and Information Security Engineering Technology Research Center Urumqi China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recently, Transformer-based methods have gained popularity in skeleton-based action recognition due to their advantages in modeling long-range dependencies. However, Transformer lacks the inductive biases towards skeletal topology and tends to capture salient features, potentially overlooking subtle inter-class variations in similar actions, leading classifications. To address these issues, we first propose Hybrid Spatial-Frequency Attention Network (HSFA-Net) with spatial and temporal structure for fine-grained action recognition. The spatial structure includes a Frequency Domain Channel Enhancement module, which utilizes the Discrete Cosine Transform to convert grouped skeleton data into frequency domain and refines it with multiple high-frequency components. Next, we propose Spatial-Frequency Attention Fusion module to integrate spatial and frequency features for a more comprehensive feature representation. In the temporal structure, we apply the concept of temporal differences to human skeletons and design a Temporal Motion-Sensitive module to highlight motion-sensitive features. Finally, we propose Temporal Frequency Attention module for richer temporal features in the frequency domain. Extensive experiments on NTU-RGB+D and NTU-RGB+D 120 datasets validate the effectiveness of HSFA-Net.

关键词： Network topology Frequency-domain analysis Speech recognition Transforms Signal processing Transformers Feature extraction Skeleton Topology Speech processing

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