Fu, Ying, Tan, Shi, Kadoch, Michel, Zhong, Jinghua, Guo, Lifeng, Zhang, Yangan, Huang, Xiaohong et Yuan, Xueguang.
2025.
« Semantic-attention enhanced DSC-transformer for lymph node ultrasound classification and remote diagnostics ».
Bioengineering, vol. 12, nº 2.
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Résumé
This study presents a novel Semantic-Attention Enhanced Dynamic Swin Convolutional Block Attention Module(CBAM) Transformer (DSC-Transformer) for lymph node ultrasound image classification. The model integrates semantic feature extraction and multi-scale attention mechanisms with the Swin Transformer architecture, enabling efficient processing of diagnostically significant regions while suppressing noise. Key innovations include semantic-driven preprocessing for localized diagnostic focus, adaptive compression for bandwidth-limited scenarios, and multi-scale attention modules for capturing both global anatomical context and local texture details. The model’s effectiveness is validated through comprehensive experiments on diverse datasets and Grad-Channel Attention Module (CAM) visualizations, demonstrating superior classification performance while maintaining high efficiency in remote diagnostic settings. This semantic-attention enhancement makes the DSC-Transformer particularly effective for telemedicine applications, representing a significant advancement in AI-driven medical image analysis with broad implications for telehealth deployment.
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Professeur: | Professeur Kadoch, Michel |
| Affiliation: | Génie électrique |
| Date de dépôt: | 18 mars 2025 15:32 |
| Dernière modification: | 27 mars 2025 18:20 |
| URI: | https://espace2.etsmtl.ca/id/eprint/30646 |
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