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- W2950754204 abstract "Cephalometric tracing method is usually used in orthodontic diagnosis and treatment planning. In this paper, we propose a deep learning based framework to automatically detect anatomical landmarks in cephalometric X-ray images. We train the deep encoder-decoder for landmark detection, and combine global landmark configuration with local high-resolution feature responses. The proposed frame-work is based on 2-stage u-net, regressing the multi-channel heatmaps for land-mark detection. In this framework, we embed attention mechanism with global stage heatmaps, guiding the local stage inferring, to regress the local heatmap patches in a high resolution. Besides, the Expansive Exploration strategy improves robustness while inferring, expanding the searching scope without increasing model complexity. We have evaluated our framework in the most widely-used public dataset of landmark detection in cephalometric X-ray images. With less computation and manually tuning, our framework achieves state-of-the-art results." @default.
- W2950754204 created "2019-06-27" @default.
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- W2950754204 date "2019-01-01" @default.
- W2950754204 modified "2023-10-10" @default.
- W2950754204 title "An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms" @default.
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- W2950754204 doi "https://doi.org/10.1007/978-3-030-32226-7_60" @default.
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