H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor Segmentation

Jun ShiHongyu KanShulan RuanZiqi ZhuMinfan ZhaoLiang QiaoZhaohui WangHong An

医学影像与智能诊断方向论文：H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Mul

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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: H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor 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: 2023
published_date: 待补充
online_date: 待补充
paper_type: Journal
publication_status: Published
volume: 待补充
issue: 待补充
pages: 待补充
article_number: 待补充
publisher: Springer
doi: 10.1007/978-3-031-43901-8_66
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: Jun Shi, Hongyu Kan, Shulan Ruan, Ziqi Zhu, Minfan Zhao, Liang Qiao, Zhaohui Wang, Hong An
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

Jun Shi*, Hongyu Kan, Shulan Ruan, Ziqi Zhu, Minfan Zhao, Liang Qiao, Zhaohui Wang, Hong An，Xudong Xue . H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor Segmentation, In proceeding of 2023 Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), Lecture Notes in Computer Science, vol 14223. Springer, 01 October 2023.（CCF B, 医学影像分析顶会）

BibTeX

@article{acsa2023_39,
  title = {H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor Segmentation},
  year = {2023},
  doi = {10.1007/978-3-031-43901-8_66}
}