Jan. 2024 · Volume 43, Issue 1 · Vol. 43 · Issue 1 · DOI 10.1109/TMI.2023.3291719
Zihan Li, Yunxiang Li, Qingde Li, Puyang Wang, Dazhou Guo, Le Lu, Dakai Jin, You Zhang
Abstract / 摘要
EnglishDeep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the prohibitive data annotation cost. To alleviate this limitation, we propose a new text-augmented medical image segmentation model LViT (Language meets ...
Author Info / 作者信息
Zihan Li
Affiliation not provided by IEEE Xplore
机构中文翻译待生成或 IEEE 未提供机构
Yunxiang Li
Affiliation not provided by IEEE Xplore
机构中文翻译待生成或 IEEE 未提供机构
Qingde Li
Affiliation not provided by IEEE Xplore
机构中文翻译待生成或 IEEE 未提供机构
Puyang Wang
Affiliation not provided by IEEE Xplore
机构中文翻译待生成或 IEEE 未提供机构
Dazhou Guo
Affiliation not provided by IEEE Xplore
机构中文翻译待生成或 IEEE 未提供机构
Le Lu
Affiliation not provided by IEEE Xplore
机构中文翻译待生成或 IEEE 未提供机构
Dakai Jin
Affiliation not provided by IEEE Xplore
机构中文翻译待生成或 IEEE 未提供机构
You Zhang
Affiliation not provided by IEEE Xplore
机构中文翻译待生成或 IEEE 未提供机构
Translation: pending
AI: pending
Article 10172039