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- Title
Point-wise spatial network for identifying carcinoma at the upper digestive and respiratory tract.
- Authors
Zhou, Lei; Jiang, Huaili; Li, Guangyao; Ding, Jiaye; Lv, Cuicui; Duan, Maoli; Wang, Wenfeng; Chen, Kongyang; Shen, Na; Huang, Xinsheng
- Abstract
Problem: Artificial intelligence has been widely investigated for diagnosis and treatment strategy design, with some models proposed for detecting oral pharyngeal, nasopharyngeal, or laryngeal carcinoma. However, no comprehensive model has been established for these regions. Aim: Our hypothesis was that a common pattern in the cancerous appearance of these regions could be recognized and integrated into a single model, thus improving the efficacy of deep learning models. Methods: We utilized a point-wise spatial attention network model to perform semantic segmentation in these regions. Results: Our study demonstrated an excellent outcome, with an average mIoU of 86.3%, and an average pixel accuracy of 96.3%. Conclusion: The research confirmed that the mucosa of oral pharyngeal, nasopharyngeal, and laryngeal regions may share a common appearance, including the appearance of tumors, which can be recognized by a single artificial intelligence model. Therefore, a deep learning model could be constructed to effectively recognize these tumors.
- Subjects
ALIMENTARY canal; ARTIFICIAL intelligence; ORAL mucosa; DEEP learning; CARCINOMA
- Publication
BMC Medical Imaging, 2023, Vol 23, Issue 1, p1
- ISSN
1471-2342
- Publication type
Article
- DOI
10.1186/s12880-023-01076-5