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- W4310588448 abstract "Dental oral disease is one of the most prevalent diseases worldwide, as of a medical analysis in The Lancet 2022[1]. The most common oral diseases worldwide are dental caries (cavities), periodontal disease, tooth loss, and overdevelopment of the jaw caused by excessive unilateral chewing. Dental radiography plays a very important role in clinical diagnosis, treatment and surgery. Automatic segmentation of medical lesions is a prerequisite for efficient clinical analysis. Therefore, accurate positioning of anatomical landmarks is a crucial technique for clinical diagnosis and treatment planning. In this paper, we propose a novel deep network to detect anatomical landmarks. Our proposed network consists of a multi-scale feature aggregation module for channel attention and a deep network for feature refinement. To demonstrate the superiority of our network, training comparisons with several popular networks are performed on the same dataset. The end result is that our network outperforms several popular networks today in both mean radial error (MRE) and successful detection rate (SDR)." @default.
- W4310588448 created "2022-12-12" @default.
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- W4310588448 date "2023-01-04" @default.
- W4310588448 modified "2023-09-27" @default.
- W4310588448 title "Head radiographic detection of feature aggregation networks with coordinated attention" @default.
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- W4310588448 doi "https://doi.org/10.1117/12.2643232" @default.
- W4310588448 hasPublicationYear "2023" @default.
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