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GL-DAE: Global-Local Denoising Auto-Encoder for Fundus Images Quality Representation Learning

GL-DAE: Global-Local Denoising Auto-Encoder for Fundus Image...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Cai, Xiangwen Lei, Haijun Xie, Hai Zhang, Guoming Lei, Baiying Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University School of Biomedical Engineering Shenzhen China Shenzhen University Shenzhen Eye Hospital Shenzhen China

ISBN: (纸本)9798350386226

Fundus image is vital for the diagnosis and monitoring of various eye diseases, where the accuracy of diagnostic results is largely determined by the quality of the obtained images. However, the performance of existing Fundus Image Quality Assessment(FIQA) methods is primarily constrained by the quantity and quality of retinal datasets, highlighting a significant challenge in the field due to the absence of a large annotated quality dataset. To alleviate this problem, we purpose the Global-Local Denoising Auto-Encoder(GL-DAE), a novel and efficient self-supervised learning pre-training approach in FIQA to overcome the scarcity of annotated fundus images. Specifically, in the pre-training phase, given a batch of unlabeled fundus images, we apply different distortion algorithms to degrade the original fundus images. The degraded fundus images are then subjected to global and local cropping, and subsequently, global and local features are extracted using a joint encoder. By reconstructing global and local fundus images, the model learns to extract features related to fundus quality. Afterwards, to complement the global reconstruction task, we further design a noise identification based contrative learning to guide the models to identify different types of degradation algorithm. Fine-tuning results across two FIQA datasets highlight the superiority of our method compared to other classical image classification methods, demonstrating its potential to advance the reliability of FIQA. © 2024 IEEE.

关键词： Self-supervised learning

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software Defect Prediction via Code Grayscale Pixel Visualization with Fusion Attention 36

Software Defect Prediction via Code Grayscale Pixel Visualiz...

引用

36th International Conference on software engineering and Knowledge engineering, SEKE 2024

作者： Qiu, Shaojian Wang, Shaosheng Rong, Wei Liao, Lili Lin, Yishen College of Software Engineering South China Agricultural University China School of Computer Science and Engineering South China University of Technology China Guangdong Tobacco Meizhou Limited Company China

ISBN: (纸本)1891706594

software defect prediction helps quality assurance teams find defects in software, thereby enhancing the reliability of the systems. In existing code-visualization-based defect prediction methods, challenges arise from mixing code information and the potential omission of critical defect features. To enhance the completeness of code features, this paper proposes a defect prediction model based on code grayscale pixel visualization with a fusion attention mechanism (Gpv2DP). Gpv2DP converts code into grayscale images and reshapes the images to a standard size, effectively alleviating the information loss problem caused by element mixing and image cropping. Furthermore, it constructs a code feature extracting network that simultaneously integrates the channel, spatial and 3D attention. We conduct empirical experiments on ten open-source Java projects from the PROMISE repository. The results show that the F-measure and AUC metrics of Gpv2DP outperform related defect prediction methods. © 2024 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： software reliability

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Advanced Bearing Fault Diagnosis Using Cuckoo Optimization and KAN Algorithms 4

Advanced Bearing Fault Diagnosis Using Cuckoo Optimization a...

引用

4th International Conference on Electronic Information engineering and computer science, EIECS 2024

作者： Wang, Lingchao Ai, Qiang Yan, Hailong Hao, Ming Li, Xiaoxin School of Information Engineering Liaoning Institute of Science and Engineering Jinzhou China College of Computer Qinghai Normal University Xining China Software College Liaoning Technical University Huludao China

ISBN: (纸本)9798331531409

This study proposes a bearing fault diagnosis method that combines the Cuckoo Optimization Algorithm (COA) with the KAN algorithm. COA, as an intelligent optimization algorithm, is primarily used to find the optimal hyperparameters of the model, thereby enhancing the efficiency and robustness of the diagnosis model. The KAN algorithm is employed to build an intelligent classification model, achieving accurate recognition of multiple fault modes. Experimental results show that the proposed method maintains high diagnostic accuracy even with limited data, providing an effective solution for bearing fault diagnosis. © 2024 IEEE.

关键词： Fault detection

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Cloth-aware Augmentation for Cloth-generalized Person Re-identification 24

Cloth-aware Augmentation for Cloth-generalized Person Re-ide...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Fangyi Ye, Mang Du, Bo National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

ISBN: (纸本)9798400706868

Person re-identification (ReID) is crucial in video surveillance, aiming to match individuals across different camera views while cloth-changing person re-identification (CC-ReID) focuses on pedestrians changing attire. Many existing CC-ReID methods overlook generalization, crucial for universality across cloth-consistent and cloth-changing scenarios. This paper pioneers exploring the cloth-generalized person re-identification (CG-ReID) task and introduces the Cloth-aware Augmentation (CaAug) strategy. Comprising domain augmentation and feature augmentation, CaAug aims to learn identity-relevant features adaptable to both scenarios. Domain augmentation involves creating diverse fictitious domains and simulating various clothing scenarios. Supervising features from different cloth domains enhances robustness and generalization against clothing changes. Additionally, for feature augmentation, element exchange introduces diversity concerning clothing changes. Regularizing the model with these augmented features strengthens resilience against clothing change uncertainty. Extensive experiments on cloth-changing datasets demonstrate the efficacy of our approach, consistently outperforming state-of-the-art methods. © 2024 ACM.

关键词： Feature extraction

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A Provable Decision Rule for Out-of-Distribution Detection 41

A Provable Decision Rule for Out-of-Distribution Detection

引用

41st International Conference on Machine Learning, ICML 2024

作者： Ma, Xinsong Zou, Xin Liu, Weiwei School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

Out-of-distribution (OOD) detection task plays the key role in reliable and safety-critical applications. Existing researches mainly devote to designing or training the powerful score function but overlook investigating the decision rule based on the proposed score function. Different from previous work, this paper aims to design a decision rule with rigorous theoretical guarantee and well empirical performance. Specifically, we provide a new insight for the OOD detection task from a hypothesis testing perspective and propose a novel generalized Benjamini Hochberg (g-BH) procedure with empirical p-values to solve the testing problem. Theoretically, the g-BH procedure controls false discovery rate (FDR) at pre-specified level. Furthermore, we derive an upper bound of the expectation of false positive rate (FPR) for the g-BH procedure based on the tailed generalized Gaussian distribution family, indicating that the FPR of g-BH procedure converges to zero in probability. Finally, the extensive experimental results verify the superiority of g-BH procedure over the traditional threshold-based decision rule on several OOD detection benchmarks. Copyright 2024 by the author(s)

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Look Deep into the Microservice System Anomaly through Very Sparse Logs 23

Look Deep into the Microservice System Anomaly through Very ...

引用

32nd ACM World Wide Web Conference, WWW 2023

作者： Jiang, Xinrui Pan, Yicheng Ma, Meng Wang, Ping School of Software and Microelectronics Peking University Beijing China School of Computer Science Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Ministry of Education China

ISBN: (纸本)9781450394161

Intensive monitoring and anomaly diagnosis have become a knotty problem for modern microservice architecture due to the dynamics of service dependency. While most previous studies rely heavily on ample monitoring metrics, we raise a fundamental but always neglected issue - the diagnostic metric integrity problem. This paper solves the problem by proposing MicroCU - a novel approach to diagnose microservice systems using very sparse API logs. We design a structure named dynamic causal curves to portray time-varying service dependencies and a temporal dynamics discovery algorithm based on Granger causal intervals. Our algorithm generates a smoother space of causal curves and designs the concept of causal unimodalization to calibrate the causality infidelities brought by missing metrics. Finally, a path search algorithm on dynamic causality graphs is proposed to pinpoint the root cause. Experiments on commercial system cases show that MicroCU outperforms many state-of-the-art approaches and reflects the superiorities of causal unimodalization to raw metric imputation. © 2023 ACM.

关键词： Anomaly detection

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Revisiting Kernel Attention with Correlated Gaussian Process Representation 40

Revisiting Kernel Attention with Correlated Gaussian Process...

引用

40th Conference on Uncertainty in Artificial Intelligence, UAI 2024

作者： Bui, Long Minh Huu, Tho Tran Dinh, Duy Nguyen, Tan Minh Hoang, Trong Nghia FPT Software AI Center Department of Mathematics National University of Singapore Singapore School of Electrical Engineering and Computer Science Washington State University United States

Transformers have increasingly become the de facto method to model sequential data with state-of-the-art performance. Due to its widespread use, being able to estimate and calibrate its modeling uncertainty is important to understand and design robust transformer models. To achieve this, previous works have used Gaussian processes (GPs) to perform uncertainty calibration for the attention units of transformers and attained notable successes. However, such approaches have to confine the transformers to the space of symmetric attention to ensure the necessary symmetric requirement of their GP’s kernel specification, which reduces the representation capacity of the model. To mitigate this restriction, we propose the Correlated Gaussian Process Transformer (CGPT), a new class of transformers whose self-attention units are modeled as cross-covariance between two correlated GPs (CGPs). This allows asymmetries in attention and can enhance the representation capacity of GP-based transformers. We also derive a sparse approximation for CGP to make it scale better. Our empirical studies show that both CGP-based and sparse CGP-based transformers achieve better performance than state-of-the-art GP-based transformers on a variety of benchmark tasks. © 2024 Proceedings of Machine Learning Research. All rights reserved.

关键词： Distribution transformers

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Radiology Report Generation via Structured Knowledge-Enhanced Multi-modal Attention and Contrastive Learning

Radiology Report Generation via Structured Knowledge-Enhance...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Xu, Dexuan Chen, Yanyuan Zhang, Jiayu Lou, Yiwei Wang, Hanpin He, Jing Huang, Yu Peking University National Engineering Research Center for S.E. School of Software & Micro. China Peking University School of Computer Science China School of Computer Science China Peking University People's Hospital Department of Rheumatology and Immunology China Peking University National Engineering Research Center for Software Engineering China

ISBN: (纸本)9798350337488

The automated generation of radiology reports has attracted significant attention in the field of bioinformatics. Currently, the main limitations of this task include insufficient utilization of prior medical knowledge, lack of efficient knowledge fusion algorithms, and less distinctiveness between different generated reports. To address these issues, we propose a novel algorithm for radiology report generation, which includes Structured Knowledge-Enhanced Multi-modal Attention (SKEMA) and Dual-Branch Contrastive Learning (DBCL) for the first time. SKEMA aims to effectively bridge the gap between visual and prior knowledge by leveraging the high-order adjacency matrix of the knowledge graph to weightedly fuse image features and knowledge features. We enhance both features through masking, and use the original features and augmented features as positive and negative samples in the dual-branch contrastive learning (DBCL). DBCL increases the differences between positive and negative samples to avoid generating templated results, and enhances the robustness of the model. Finally, we conducted experiments to demonstrate the effectiveness of our model on two public radiology datasets, IU-Xray and MIMIC-CXR. Our model outperformed previous baseline methods on both datasets and achieved excellent evaluation scores. © 2023 IEEE.

关键词： Contrastive Learning Knowledge Graph Multi-modal Fusion Radiology Report Generation

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HGTRDA: Enhancing Prediction of ncRNA-Mediated Drug Resistance with Hypergraph Transformer

HGTRDA: Enhancing Prediction of ncRNA-Mediated Drug Resistan...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Che He, Ruohui Fan, Yanhao Hu, Xiaowen Qian, Yurong Deng, Lei People's Hospital of Ningyuan Clinical Pharmacy Yongzhou China Xinjiang University School of Software Urumqi China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350386226

Exploring the intricate connections between non-coding RNAs (ncRNAs) and drug resistance is crucial for understanding the molecular mechanisms behind drug resistance, identifying novel drug development targets, and uncovering key biomarkers to optimize therapeutic strategies. Traditional biological assays face significant challenges, including high costs and lengthy timelines, prompting the need for advanced computational methods to predict ncRNA-drug resistance associations. In this study, we introduce HGTRDA, a novel computational framework designed to predict potential associations between ncRNAs and drug resistance. HGTRDA leverages LightGCN to generate node representations that capture topological information from the surrounding node neighborhood. These representations are then dynamically optimized using a global hypergraph transformer to model the relationships between ncRNAs and drug resistance. To enhance the quality of the learned embeddings, HGTRDA employs self-supervised learning to fine-tune topology-aware embeddings, reducing the impact of noise and improving representation quality. The final association scores between ncRNAs and drugs are computed using an inner product method. Empirical evaluations on the ncRNADrug database demonstrate that HGTRDA outperforms six contemporary state-of-the-art methods in predicting ncRNA-drug resistance associations. Furthermore, case studies illustrate the practical utility of HGTRDA as a predictive tool in real-world scenarios. The code and dataset for HGTRDA are freely available at https://***/one-melon/HGTRDA. © 2024 IEEE.

关键词： Self-supervised learning

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A Theoretical Analysis of Backdoor Poisoning Attacks in Convolutional Neural Networks 41

A Theoretical Analysis of Backdoor Poisoning Attacks in Conv...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Boqi Liu, Weiwei School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

The rising threat of backdoor poisoning attacks (BPAs) on Deep Neural Networks (DNNs) has become a significant concern in recent years. In such attacks, the adversaries strategically target a specific class and generate a poisoned training set. The neural network (NN), well-trained on the poisoned training set, is able to predict any input with the trigger pattern as the targeted label, while maintaining accurate outputs for clean inputs. However, why the BPAs work remains less explored. To fill this gap, we employ a dirty-label attack and conduct a detailed analysis of BPAs in a two-layer convolutional neural network. We provide theoretical insights and results on the effectiveness of BPAs. Our experimental results on two real-world datasets validate our theoretical findings. Copyright 2024 by the author(s)

关键词： Convolutional neural networks

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