相关实验视频

Updated: May 6, 2026

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

9.7K

基于伪标签纠正和不确定性否定的弱监督的核细分.

Xipeng Pan1, Shilong Song1, Zhenbing Liu1

  • 1Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, 541004, Guangxi, China.

Artificial intelligence in medicine
|April 2, 2025
PubMed
概括

这项研究引入了一种新的两阶段弱监督模型,用于仅使用点注释的组织病理图像中的核细分. 与现有的以点标签为基础的方法相比,该方法实现了更高的性能.

关键词:
核心细分的核心细分.点的注释点的注释伪标签拒绝使用.监督的弱点 监督的弱点

更多相关视频

Deep Learning-Based Segmentation of Cryo-Electron Tomograms10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.4K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K

相关概念视频

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
1.2K