Papillary Thyroid Carcinoma Semantic Segmentation Using Multi-Scale Adaptive Convolutional Network With Dual Decoders
Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Currently, pathologists diagnose the PTC by interpreting their nuclei. However, the existing diagnosis method is difficult to interpret, especially for the cases falling in the borderline zones. According to the advances in...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10848109/ |
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| author | Thanat Payatsuporn Pittipol Kantavat Nichthida Tangnuntachai Nopporn Tipparawong Waratchanok Techapapa Boonserm Kijsirikul Somboon Keelawat |
| author_facet | Thanat Payatsuporn Pittipol Kantavat Nichthida Tangnuntachai Nopporn Tipparawong Waratchanok Techapapa Boonserm Kijsirikul Somboon Keelawat |
| author_sort | Thanat Payatsuporn |
| collection | DOAJ |
| description | Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Currently, pathologists diagnose the PTC by interpreting their nuclei. However, the existing diagnosis method is difficult to interpret, especially for the cases falling in the borderline zones. According to the advances in artificial intelligence (AI) technology, semantic segmentation is used to support medical personnel. This study proposes Multi-scale Adaptive Convolutional Network with DBUNet (MSAC-DBUNet) and Multi-scale Adaptive Convolutional Network with Dual Decoders (MSAC-DD), which are more accurate and faster than the traditional networks. The experimental result shows that MSAC-DBUNet achieves good PTC segmentation outcome, and MSAC-DD provides a comparable score while reducing computation time and GPU usage compared to MSAC-DBUNet. It can be used for further studies to support the pathology practice. |
| format | Article |
| id | doaj-art-f5a18ae5c8154871a338c03d87ac01e6 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f5a18ae5c8154871a338c03d87ac01e62025-01-31T00:00:47ZengIEEEIEEE Access2169-35362025-01-0113173401735310.1109/ACCESS.2025.353250510848109Papillary Thyroid Carcinoma Semantic Segmentation Using Multi-Scale Adaptive Convolutional Network With Dual DecodersThanat Payatsuporn0https://orcid.org/0009-0007-3197-0981Pittipol Kantavat1https://orcid.org/0000-0002-5406-4697Nichthida Tangnuntachai2https://orcid.org/0009-0003-4433-6681Nopporn Tipparawong3https://orcid.org/0009-0008-5349-1380Waratchanok Techapapa4https://orcid.org/0009-0001-7236-6373Boonserm Kijsirikul5https://orcid.org/0000-0002-9046-7151Somboon Keelawat6https://orcid.org/0000-0002-4180-7914Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandDepartment of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandPrecision Pathology of Neoplasia Research Group, Department of Pathology, Chulalongkorn University, Bangkok, ThailandDepartment of Pathology, Faculty of Medicine, Chulalongkorn University, Bangkok, ThailandDepartment of Pathology, Faculty of Medicine, Chulalongkorn University, Bangkok, ThailandDepartment of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandDepartment of Pathology, Faculty of Medicine, Chulalongkorn University, Bangkok, ThailandPapillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Currently, pathologists diagnose the PTC by interpreting their nuclei. However, the existing diagnosis method is difficult to interpret, especially for the cases falling in the borderline zones. According to the advances in artificial intelligence (AI) technology, semantic segmentation is used to support medical personnel. This study proposes Multi-scale Adaptive Convolutional Network with DBUNet (MSAC-DBUNet) and Multi-scale Adaptive Convolutional Network with Dual Decoders (MSAC-DD), which are more accurate and faster than the traditional networks. The experimental result shows that MSAC-DBUNet achieves good PTC segmentation outcome, and MSAC-DD provides a comparable score while reducing computation time and GPU usage compared to MSAC-DBUNet. It can be used for further studies to support the pathology practice.https://ieeexplore.ieee.org/document/10848109/Papillary thyroid carcinomahistopathology imagepathologydeep learningconvolution neural networksemantic segmentation |
| spellingShingle | Thanat Payatsuporn Pittipol Kantavat Nichthida Tangnuntachai Nopporn Tipparawong Waratchanok Techapapa Boonserm Kijsirikul Somboon Keelawat Papillary Thyroid Carcinoma Semantic Segmentation Using Multi-Scale Adaptive Convolutional Network With Dual Decoders IEEE Access Papillary thyroid carcinoma histopathology image pathology deep learning convolution neural network semantic segmentation |
| title | Papillary Thyroid Carcinoma Semantic Segmentation Using Multi-Scale Adaptive Convolutional Network With Dual Decoders |
| title_full | Papillary Thyroid Carcinoma Semantic Segmentation Using Multi-Scale Adaptive Convolutional Network With Dual Decoders |
| title_fullStr | Papillary Thyroid Carcinoma Semantic Segmentation Using Multi-Scale Adaptive Convolutional Network With Dual Decoders |
| title_full_unstemmed | Papillary Thyroid Carcinoma Semantic Segmentation Using Multi-Scale Adaptive Convolutional Network With Dual Decoders |
| title_short | Papillary Thyroid Carcinoma Semantic Segmentation Using Multi-Scale Adaptive Convolutional Network With Dual Decoders |
| title_sort | papillary thyroid carcinoma semantic segmentation using multi scale adaptive convolutional network with dual decoders |
| topic | Papillary thyroid carcinoma histopathology image pathology deep learning convolution neural network semantic segmentation |
| url | https://ieeexplore.ieee.org/document/10848109/ |
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