Scholarlink

<p>In recent years, the progress of artificial intelligence has greatly advanced computer-assisted intervention, surgical learning, and postoperative surgical video analysis techniques, greatly improving the skill levels of surgeons and overall outcomes. Deep learning based endoscopic surgery phase recognition has a very high dependency on large-scale datasets and annotations. This study introduces the Renji endoscopic submucosal dissection (ESD) video dataset for early gastric cancer (EGC), comprising 20 ESD endoscopic videos and 66,656 phase recognition annotations jointly annotated by three endoscopists. To the best of our knowledge, this is the first publicly available ESD dataset for the treatment of EGC, and we believe this work will contribute to the standardization of ESD dataset construction. The dataset and annotations are publicly available in Figshare.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Renji endoscopic submucosal dissection video data set for early gastric cancer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月10日

查看全文
  • Jinnan Chen,
  • Xiangning Zhang,
  • Chunjiang Gu,
  • Tang Cao,
  • Jinneng Wang,
  • Zhao Li,
  • Yiming Song,
  • Liuyi Yang,
  • Zhengjie Zhang,
  • Qingwei Zhang,
  • Dahong Qian,
  • Xiaobo Li

摘要

In recent years, the progress of artificial intelligence has greatly advanced computer-assisted intervention, surgical learning, and postoperative surgical video analysis techniques, greatly improving the skill levels of surgeons and overall outcomes. Deep learning based endoscopic surgery phase recognition has a very high dependency on large-scale datasets and annotations. This study introduces the Renji endoscopic submucosal dissection (ESD) video dataset for early gastric cancer (EGC), comprising 20 ESD endoscopic videos and 66,656 phase recognition annotations jointly annotated by three endoscopists. To the best of our knowledge, this is the first publicly available ESD dataset for the treatment of EGC, and we believe this work will contribute to the standardization of ESD dataset construction. The dataset and annotations are publicly available in Figshare.