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Updated: Jul 15, 2025
Electromagnetic Navigation Transthoracic Nodule Localization for Minimally Invasive Thoracic Surgery
Published on: May 4, 2022
Point-wise spatial network for identifying carcinoma at the upper digestive and respiratory tract
Lei Zhou1, Huaili Jiang1, Guangyao Li1
1Department of Otorhinolaryngology-Head and Neck Surgery, Zhongshan Hospital Affiliated to Fudan University, Xuhui District, 180 Fenglin Road, , Shanghai, 200032, P. R. China.
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.
