An Innovative Solution Based on TSCA-ViT for Osteosarcoma Diagnosis in Resource-Limited Settings
Identifying and managing osteosarcoma pose significant challenges, especially in resource-constrained developing nations. Advanced diagnostic methods involve isolating the nucleus from cancer cells for comprehensive analysis. However, two main challenges persist: mitigating image noise during the ca...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
|
| Series: | Biomedicines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9059/11/10/2740 |
| _version_ | 1797574625970880512 |
|---|---|
| author | Zengxiao He Jun Liu Fangfang Gou Jia Wu |
| author_facet | Zengxiao He Jun Liu Fangfang Gou Jia Wu |
| author_sort | Zengxiao He |
| collection | DOAJ |
| description | Identifying and managing osteosarcoma pose significant challenges, especially in resource-constrained developing nations. Advanced diagnostic methods involve isolating the nucleus from cancer cells for comprehensive analysis. However, two main challenges persist: mitigating image noise during the capture and transmission of cellular sections, and providing an efficient, accurate, and cost-effective solution for cell nucleus segmentation. To tackle these issues, we introduce the Twin-Self and Cross-Attention Vision Transformer (TSCA-ViT). This pioneering AI-based system employs a directed filtering algorithm for noise reduction and features an innovative transformer architecture with a twin attention mechanism for effective segmentation. The model also incorporates cross-attention-enabled skip connections to augment spatial information. We evaluated our method on a dataset of 1000 osteosarcoma pathology slide images from the Second People’s Hospital of Huaihua, achieving a remarkable average precision of 97.7%. This performance surpasses traditional methodologies. Furthermore, TSCA-ViT offers enhanced computational efficiency owing to its fewer parameters, which results in reduced time and equipment costs. These findings underscore the superior efficacy and efficiency of TSCA-ViT, offering a promising approach for addressing the ongoing challenges in osteosarcoma diagnosis and treatment, particularly in settings with limited resources. |
| first_indexed | 2024-03-10T21:25:59Z |
| format | Article |
| id | doaj.art-ee1b16e60db54c4ba30565de806d42f5 |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-10T21:25:59Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-ee1b16e60db54c4ba30565de806d42f52023-11-19T15:46:25ZengMDPI AGBiomedicines2227-90592023-10-011110274010.3390/biomedicines11102740An Innovative Solution Based on TSCA-ViT for Osteosarcoma Diagnosis in Resource-Limited SettingsZengxiao He0Jun Liu1Fangfang Gou2Jia Wu3School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaThe Second People’s Hospital of Huaihua, Huaihua 418000, ChinaState Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaIdentifying and managing osteosarcoma pose significant challenges, especially in resource-constrained developing nations. Advanced diagnostic methods involve isolating the nucleus from cancer cells for comprehensive analysis. However, two main challenges persist: mitigating image noise during the capture and transmission of cellular sections, and providing an efficient, accurate, and cost-effective solution for cell nucleus segmentation. To tackle these issues, we introduce the Twin-Self and Cross-Attention Vision Transformer (TSCA-ViT). This pioneering AI-based system employs a directed filtering algorithm for noise reduction and features an innovative transformer architecture with a twin attention mechanism for effective segmentation. The model also incorporates cross-attention-enabled skip connections to augment spatial information. We evaluated our method on a dataset of 1000 osteosarcoma pathology slide images from the Second People’s Hospital of Huaihua, achieving a remarkable average precision of 97.7%. This performance surpasses traditional methodologies. Furthermore, TSCA-ViT offers enhanced computational efficiency owing to its fewer parameters, which results in reduced time and equipment costs. These findings underscore the superior efficacy and efficiency of TSCA-ViT, offering a promising approach for addressing the ongoing challenges in osteosarcoma diagnosis and treatment, particularly in settings with limited resources.https://www.mdpi.com/2227-9059/11/10/2740cell nucleus segmentationosteosarcomamachine learningimage noise reductioncomputational efficiencypathology slide images |
| spellingShingle | Zengxiao He Jun Liu Fangfang Gou Jia Wu An Innovative Solution Based on TSCA-ViT for Osteosarcoma Diagnosis in Resource-Limited Settings Biomedicines cell nucleus segmentation osteosarcoma machine learning image noise reduction computational efficiency pathology slide images |
| title | An Innovative Solution Based on TSCA-ViT for Osteosarcoma Diagnosis in Resource-Limited Settings |
| title_full | An Innovative Solution Based on TSCA-ViT for Osteosarcoma Diagnosis in Resource-Limited Settings |
| title_fullStr | An Innovative Solution Based on TSCA-ViT for Osteosarcoma Diagnosis in Resource-Limited Settings |
| title_full_unstemmed | An Innovative Solution Based on TSCA-ViT for Osteosarcoma Diagnosis in Resource-Limited Settings |
| title_short | An Innovative Solution Based on TSCA-ViT for Osteosarcoma Diagnosis in Resource-Limited Settings |
| title_sort | innovative solution based on tsca vit for osteosarcoma diagnosis in resource limited settings |
| topic | cell nucleus segmentation osteosarcoma machine learning image noise reduction computational efficiency pathology slide images |
| url | https://www.mdpi.com/2227-9059/11/10/2740 |
| work_keys_str_mv | AT zengxiaohe aninnovativesolutionbasedontscavitforosteosarcomadiagnosisinresourcelimitedsettings AT junliu aninnovativesolutionbasedontscavitforosteosarcomadiagnosisinresourcelimitedsettings AT fangfanggou aninnovativesolutionbasedontscavitforosteosarcomadiagnosisinresourcelimitedsettings AT jiawu aninnovativesolutionbasedontscavitforosteosarcomadiagnosisinresourcelimitedsettings AT zengxiaohe innovativesolutionbasedontscavitforosteosarcomadiagnosisinresourcelimitedsettings AT junliu innovativesolutionbasedontscavitforosteosarcomadiagnosisinresourcelimitedsettings AT fangfanggou innovativesolutionbasedontscavitforosteosarcomadiagnosisinresourcelimitedsettings AT jiawu innovativesolutionbasedontscavitforosteosarcomadiagnosisinresourcelimitedsettings |