In recent years, multi-view multi-label learning (MVML) has gained popularity due to its close resemblance to real-world scenarios. However, the challenge of selecting informative features to ensure both performance a...
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Federated learning (FL) enables collaborative model training across multiple medical institutions to ensure data security. However, due to the variations in medical imaging equipment and regions at different medical i...
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ISBN:
(数字)9798350386226
ISBN:
(纸本)9798350386233
Federated learning (FL) enables collaborative model training across multiple medical institutions to ensure data security. However, due to the variations in medical imaging equipment and regions at different medical institutions, FL methods usually suffer from insufficient data annotations and irrelevant noise within private datasets. To address these issues, a robust federated semi-supervised learning method via pseudo-label filtering (PFRFed) is introduced to utilize unlabeled data while mitigating the impact of noise data. Compared with existing federated semi-supervised learning methods, we propose a pseudo-label filtering mechanism with double dynamic thresholds, which allows the model to adopt more unlabeled data by adjusting the confidence and entropy thresholds at each stage of model training. Moreover, to reduce the degradation caused by noise data in private datasets from different clients, a noise-tolerant loss function and a grouping aggregation method based on the local model similarity are employed. The comparative experiments demonstrate the effectiveness of PFRFed, which has achieved the best classification accuracy of 95.20% and 88.72% on two public medical datasets. Also, PFRFed exhibits heightened resilience to variations in noisy data ratio and labeled data ratio, reaffirming its versatility and robustness.
The purpose of partial multi-label feature selection is to select the most representative feature subset, where the data comes from partial multi-label datasets that have label ambiguity issues. For label disambiguati...
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Single-cell RNA sequencing (scRNA-seq) technology establishes a unique view for elucidating cellular heterogeneity in various biological systems. Yet the scRNA-seq data is compromised by a high dropout rate due to the...
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Human pose estimation in videos remains a challenge, largely due to the reliance on extensive manual annotation of large datasets, which is expensive and labor-intensive. Furthermore, existing approaches often struggl...
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Positive and Unlabeled (PU) learning targets inducing a binary classifier from weak training datasets of positive and unlabeled instances, which arise in many real-world applications. In this paper, we propose a novel...
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Nowadays, deep learning has made rapid progress in the field of multi-exposure image fusion. However, it is still challenging to extract available features while retaining texture details and color. To address this di...
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This paper simulates the cuckoo incubation process and flight path to optimize the Wavelet Neural Network(WNN)model,and proposes a parking prediction algorithm based on WNN and improved Cuckoo Search(CS)***,the initia...
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This paper simulates the cuckoo incubation process and flight path to optimize the Wavelet Neural Network(WNN)model,and proposes a parking prediction algorithm based on WNN and improved Cuckoo Search(CS)***,the initialization parameters are provided to optimize the WNN using the improved *** traditional CS algorithm adopts the strategy of overall update and evaluation,but does not consider its own information,so the convergence speed is very *** proposed algorithm employs the evaluation strategy of group update,which not only retains the advantage of fast convergence of the dimension-by-dimension update evaluation strategy,but also increases the mutual relationship between the nests and reduces the overall running ***,we use the WNN model to predict parking *** proposed algorithm is compared with six different heuristic algorithms in five *** experimental results show that the proposed algorithm is superior to other algorithms in terms of running time and accuracy.
Model-based diagnosis (MBD) with multiple abnormal observations poses a significant challenge. To address this, we propose the Dual Principles with Decision Node (DPDN) algorithm. DPDN encompasses two novel principles...
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The purpose of partial multi-label feature selection is to select the most representative feature subset, where the data comes from partial multi-label datasets that have label ambiguity issues. For label disambiguati...
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