• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE Conference on Computer Vision and Pattern Recognition Workshops >Delineation of Skin Strata in Reflectance Confocal Microscopy Images with Recurrent Convolutional Networks
【24h】

Delineation of Skin Strata in Reflectance Confocal Microscopy Images with Recurrent Convolutional Networks

机译：用复发卷积网络描绘反射率的暗圈共聚焦显微镜图像中的皮肤层

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Reflectance confocal microscopy (RCM) is an effective, non-invasive pre-screening tool for cancer diagnosis. However, acquiring and reading RCM images requires extensive training and experience, and novice clinicians exhibit high variance in diagnostic accuracy. Consequently, there is a compelling need for quantitative tools to standardize image acquisition and analysis. In this study, we use deep recurrent convolutional neural networks to delineate skin strata in stacks of RCM images collected at consecutive depths. To perform diagnostic analysis, clinicians collect RCM images at 4-5 specific layers in the tissue. Our model automates this process by discriminating between RCM images of different layers. Testing our model on an expert labeled dataset of 504 RCM stacks, we achieve 87.97% classification accuracy, and a 9-fold reduction in the number of anatomically impossible errors compared to the previous state-of-the-art.
机译：反射率共聚焦显微镜（RCM）是一种有效的非侵入性预筛分工具，用于癌症诊断。但是，获取和阅读RCM图像需要广泛的培训和经验，新手临床医生以诊断准确性的差异很大。因此，有一个令人信服的需要定量工具来标准化图像采集和分析。在这项研究中，我们使用深频卷积神经网络在连续深度收集的RCM图像堆叠中描绘皮肤层。为了执行诊断分析，临床医生在组织中的4-5个特定层中收集RCM图像。我们的模型通过区分不同层的RCM图像来自动化此过程。测试我们的模型标记为504 rcm堆栈的专家数据集，我们达到了87.97 ％的分类准确性，与以前最先进的误差相比，剖视不可能的误差的数量减少了9倍。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号