LCANet: Learnable Connected Attention Network for Human Identification Using Dental Images
| العنوان: | LCANet: Learnable Connected Attention Network for Human Identification Using Dental Images |
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| المؤلفون: | Fei Fan, Yancun Lai, Zhen-hua Deng, Peixi Liao, Hu Chen, Wenchi Ke, Yi Zhang, Qingsong Wu |
| المصدر: | IEEE Transactions on Medical Imaging. 40:905-915 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Radiological and Ultrasound Technology, Matching (graph theory), Artificial neural network, Channel (digital image), Computer science, business.industry, Feature extraction, Pattern recognition, 030218 nuclear medicine & medical imaging, Computer Science Applications, 03 medical and health sciences, Identification (information), 0302 clinical medicine, Code (cryptography), Forensic Anthropology, Humans, Neural Networks, Computer, Artificial intelligence, Electrical and Electronic Engineering, Layer (object-oriented design), business, Software |
| الوصف: | Forensic odontology is regarded as an important branch of forensics dealing with human identification based on dental identification. This paper proposes a novel method that uses deep convolution neural networks to assist in human identification by automatically and accurately matching 2-D panoramic dental X-ray images. Designed as a top-down architecture, the network incorporates an improved channel attention module and a learnable connected module to better extract features for matching. By integrating associated features among all channel maps, the channel attention module can selectively emphasize interdependent channel information, which contributes to more precise recognition results. The learnable connected module not only connects different layers in a feed-forward fashion but also searches the optimal connections for each connected layer, resulting in automatically and adaptively learning the connections among layers. Extensive experiments demonstrate that our method can achieve new state-of-the-art performance in human identification using dental images. Specifically, the method is tested on a dataset including 1,168 dental panoramic images of 503 different subjects, and its dental image recognition accuracy for human identification reaches 87.21% rank-1 accuracy and 95.34% rank-5 accuracy. Code has been released on Github. ( https://github.com/cclaiyc/TIdentifyTest ) |
| تدمد: | 1558-254X 0278-0062 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c0b1bc4ab85a023042817ff4babc7940Test https://doi.org/10.1109/tmi.2020.3041452Test |
| حقوق: | CLOSED |
| رقم الانضمام: | edsair.doi.dedup.....c0b1bc4ab85a023042817ff4babc7940 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 1558254X 02780062 |
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