Search alternatives:
significant challenges » significant challenge (Expand Search), significant changes (Expand Search)
challenges decrease » challenges case (Expand Search)
small decrease » small increased (Expand Search)
step decrease » sizes decrease (Expand Search), teer decrease (Expand Search), we decrease (Expand Search)
significant challenges » significant challenge (Expand Search), significant changes (Expand Search)
challenges decrease » challenges case (Expand Search)
small decrease » small increased (Expand Search)
step decrease » sizes decrease (Expand Search), teer decrease (Expand Search), we decrease (Expand Search)
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Architecture of Swin-T model.
Published 2024“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”
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Model the experimental results curve.
Published 2024“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”
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Results of comparison experiments.
Published 2024“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”
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Architecture of Swin Transformer Block.
Published 2024“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”
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Disease distribution map of the GZDL-BD.
Published 2024“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”
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Token merging module.
Published 2024“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”
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Comparative results of the ablation experiments.
Published 2024“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”
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