PSPNet
A semantic segmentation model that captures multi-scale context information through a pyramid pooling module, making it suitable for scene parsing.
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A study systematically compared four mainstream segmentation models—PSPNet, DeepLabV3, UNet, and UNet++—under unified experimental conditions for dental X-ray image segmentation.
PSPNet's pyramid pooling module pools the feature map into regions of multiple different sizes (such as 1×1, 2×2, 3×3, 6×6), then upsamples the pooling results of each scale and concatenates them, to simultaneously capture local details and global contextual information.
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PSPNet's pyramid pooling module pools the feature map into regions of multiple different sizes (such as 1×1, 2×2, 3×3, 6×6), then upsamples the pooling results of each scale and concatenates them, to simultaneously capture local details and global contextual information.
50%UnverifiedA study systematically compared four mainstream segmentation models—PSPNet, DeepLabV3, UNet, and UNet++—under unified experimental conditions for dental X-ray image segmentation.
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