ITUnet: Integration Of Transformers And Unet For Organs-At-Risk Segmentation

Hongyu KanJun ShiMinfan ZhaoWenting HanHong AnZhaohui Wang

医学影像与智能诊断方向论文：ITUnet: Integration Of Transformers And Unet For Organs-At-Risk Segmenta

↗在线页面Official page ◇DOI

内容导读Reading note

分类与摘要Category & abstract

聚焦医学影像分割、诊断或数据选择，体现 AI 方法在临床相关任务中的应用。

Metadata

完整论文信息Full record

title: ITUnet: Integration Of Transformers And Unet For Organs-At-Risk Segmentation
title_zh: 待补充
abstract: 待补充
abstract_zh: 待补充
keywords: Medical, 学位认定 B, CCF B
year: 2022
published_date: 待补充
online_date: 待补充
paper_type: Conference
publication_status: Published
volume: 待补充
issue: 待补充
pages: 待补充
article_number: 待补充
publisher: IEEE
doi: 10.1109/embc48229.2022.9871945
research_area: 医学影像与智能诊断
tags: Medical, 学位认定 B, CCF B
category: 医学影像与智能诊断
summary: 聚焦医学影像分割、诊断或数据选择，体现 AI 方法在临床相关任务中的应用。
authors: Hongyu Kan, Jun Shi, Minfan Zhao, Wenting Han, Hong An, Zhaohui Wang
corresponding_authors: 待补充
affiliations: 待补充
funding: 待补充

Research area

医学影像与智能诊断

Citation

Hongyu Kan*, Jun Shi, Minfan Zhao，Wenting Han*, Hong An, Zhaohui Wang，ITUnet: Integration Of Transformers And Unet For Organs-At-Risk Segmentation，in the proceeding of 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)，Scotland UK, July 11-15, 2022（CCF B）

BibTeX

@article{acsa2022_50,
  title = {ITUnet: Integration Of Transformers And Unet For Organs-At-Risk Segmentation},
  year = {2022},
  doi = {10.1109/embc48229.2022.9871945}
}