Point-wise spatial network for identifying carcinoma at the upper digestive and respiratory tract

BMC Medical Imaging

Research

Maoli Duan¹  Lei Zhou²  Huaili Jiang²  Guangyao Li²  Na Shen²  Cuicui Lv²  Jiaye Ding²  Xinsheng Huang²  Wenfeng Wang³  Kongyang Chen⁴ 

[1] Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden;Department of Otolaryngology Head and Neck Surgery, Karolinska University Hospital, 171 76, Stockholm, Sweden;Department of Otorhinolaryngology-Head and Neck Surgery, Zhongshan Hospital Affiliated to Fudan University, Xuhui District, 180 Fenglin Road,, 200032, Shanghai, P. R. China;Institute of Artificial Intelligence and Blockchain, Guangzhou University, 510006, Guangzhou, P. R. China;Institute of Artificial Intelligence and Blockchain, Guangzhou University, 510006, Guangzhou, P. R. China;Pazhou Lab, 510330, Guangzhou, P. R. China;

关键词: Artificial intelligence;    Oral pharynx;    Hypopharynx;    Larynx;    Nasopharynx;   

DOI  :  10.1186/s12880-023-01076-5

 received in 2022-10-12, accepted in 2023-08-07,  发布年份 2023

来源: Springer

【 摘 要 】

ProblemArtificial 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.AimOur 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.MethodsWe utilized a point-wise spatial attention network model to perform semantic segmentation in these regions.ResultsOur study demonstrated an excellent outcome, with an average mIoU of 86.3%, and an average pixel accuracy of 96.3%.ConclusionThe 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.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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