會議論文
| 學年 | 113 |
|---|---|
| 學期 | 1 |
| 發表日期 | 2024-08-18 |
| 作品名稱 | Integration Self-attention with UNet for Tumor Segmentation in Breast Ultrasound |
| 作品名稱（其他語言） | |
| 著者 | Chen, Chii-jen; Chiou, Yu-jie; Hsu, Shao-hua; Chang, Yu-cheng |
| 作品所屬單位 | |
| 出版者 | |
| 會議名稱 | 第三十七屆電腦視覺,圖學暨影像處理研討會 (CVGIP2024) |
| 會議地點 | 花蓮縣，臺灣 |
| 摘要 | UNet has achieved remarkable results and made significant contributions in the semantic segmentation of medical images. Recently, the rapid development of large language models has brought new milestones to the field of artificial intelligence, inspiring us to apply their successful experiences to neural networks in computer vision. This study incorporates the self-attention mechanism into the UNet architecture, enabling each pixel to better understand global information, thereby enhancing the relationships between features. We conducted experiments on medical image datasets, and the results indicate that the enhanced model significantly improves segmentation accuracy and robustness. Our research showcases the potential of the self-attention mechanism in enhancing the performance of medical image segmentation. |
| 關鍵字 | UNet;Self-attention;Segmentation;Breast ultrasound |
| 語言 | en |
| 收錄於 | |
| 會議性質 | 國內 |
| 校內研討會地點 | 無 |
| 研討會時間 | 20240818~20240820 |
| 通訊作者 | Chang, Yu-cheng |
| 國別 | TWN |
| 公開徵稿 | |
| 出版型式 | |
| 出處 | |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126134 ) |