Head radiographic detection of feature aggregation networks with coordinated attention

Xinwang Shao; Zhihong Chen; Jia Lu; Haiwei Zhang; Yinping Miao; Wenhan Song

doi:10.1117/12.2643232

4 January 2023 Head radiographic detection of feature aggregation networks with coordinated attention

Xinwang Shao, Zhihong Chen, Jia Lu, Haiwei Zhang, Yinping Miao, Wenhan Song

Proceedings Volume 12317, Optoelectronic Imaging and Multimedia Technology IX; 123170T (2023) https://doi.org/10.1117/12.2643232
Event: SPIE/COS Photonics Asia, 2022, Online Only

Conference Poster

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).

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Xinwang Shao, Zhihong Chen, Jia Lu, Haiwei Zhang, Yinping Miao, and Wenhan Song "Head radiographic detection of feature aggregation networks with coordinated attention", Proc. SPIE 12317, Optoelectronic Imaging and Multimedia Technology IX, 123170T (4 January 2023); https://doi.org/10.1117/12.2643232

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