Point-wise spatial network for identifying carcinoma at the upper digestive and respiratory tract
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 hypothes...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-09-01
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| Series: | BMC Medical Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12880-023-01076-5 |
| _version_ | 1797555641951191040 |
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| author | Lei Zhou Huaili Jiang Guangyao Li Jiaye Ding Cuicui Lv Maoli Duan Wenfeng Wang Kongyang Chen Na Shen Xinsheng Huang |
| author_facet | Lei Zhou Huaili Jiang Guangyao Li Jiaye Ding Cuicui Lv Maoli Duan Wenfeng Wang Kongyang Chen Na Shen Xinsheng Huang |
| author_sort | Lei Zhou |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T16:50:23Z |
| format | Article |
| id | doaj.art-08b6d0f3412e4f06baa2d28d53895568 |
| institution | Directory Open Access Journal |
| issn | 1471-2342 |
| language | English |
| last_indexed | 2024-03-10T16:50:23Z |
| publishDate | 2023-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj.art-08b6d0f3412e4f06baa2d28d538955682023-11-20T11:18:52ZengBMCBMC Medical Imaging1471-23422023-09-012311810.1186/s12880-023-01076-5Point-wise spatial network for identifying carcinoma at the upper digestive and respiratory tractLei Zhou0Huaili Jiang1Guangyao Li2Jiaye Ding3Cuicui Lv4Maoli Duan5Wenfeng Wang6Kongyang Chen7Na Shen8Xinsheng Huang9Department of Otorhinolaryngology-Head and Neck Surgery, Zhongshan Hospital Affiliated to Fudan University, Xuhui DistrictDepartment of Otorhinolaryngology-Head and Neck Surgery, Zhongshan Hospital Affiliated to Fudan University, Xuhui DistrictDepartment of Otorhinolaryngology-Head and Neck Surgery, Zhongshan Hospital Affiliated to Fudan University, Xuhui DistrictDepartment of Otorhinolaryngology-Head and Neck Surgery, Zhongshan Hospital Affiliated to Fudan University, Xuhui DistrictDepartment of Otorhinolaryngology-Head and Neck Surgery, Zhongshan Hospital Affiliated to Fudan University, Xuhui DistrictDepartment of Clinical Science, Intervention and Technology, Karolinska InstitutetInstitute of Artificial Intelligence and Blockchain, Guangzhou UniversityInstitute of Artificial Intelligence and Blockchain, Guangzhou UniversityDepartment of Otorhinolaryngology-Head and Neck Surgery, Zhongshan Hospital Affiliated to Fudan University, Xuhui DistrictDepartment of Otorhinolaryngology-Head and Neck Surgery, Zhongshan Hospital Affiliated to Fudan University, Xuhui DistrictAbstract 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.https://doi.org/10.1186/s12880-023-01076-5Artificial intelligenceOral pharynxHypopharynxLarynxNasopharynx |
| spellingShingle | Lei Zhou Huaili Jiang Guangyao Li Jiaye Ding Cuicui Lv Maoli Duan Wenfeng Wang Kongyang Chen Na Shen Xinsheng Huang Point-wise spatial network for identifying carcinoma at the upper digestive and respiratory tract BMC Medical Imaging Artificial intelligence Oral pharynx Hypopharynx Larynx Nasopharynx |
| title | Point-wise spatial network for identifying carcinoma at the upper digestive and respiratory tract |
| title_full | Point-wise spatial network for identifying carcinoma at the upper digestive and respiratory tract |
| title_fullStr | Point-wise spatial network for identifying carcinoma at the upper digestive and respiratory tract |
| title_full_unstemmed | Point-wise spatial network for identifying carcinoma at the upper digestive and respiratory tract |
| title_short | Point-wise spatial network for identifying carcinoma at the upper digestive and respiratory tract |
| title_sort | point wise spatial network for identifying carcinoma at the upper digestive and respiratory tract |
| topic | Artificial intelligence Oral pharynx Hypopharynx Larynx Nasopharynx |
| url | https://doi.org/10.1186/s12880-023-01076-5 |
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