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摘要
En 中文
The time and cost of annotating ground-truth images and network training are major challenges to utilizing machine learning to automate the mining of volume electron microscopy data. In this issue, Gallusser et al. (2023. J. Cell Biol. https://doi.org/10. 1083/jcb.202208005) present a less computationally intense pipeline to detect a single type of organelle using a limited number of loosely annotated images.
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对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.4
论文数:
1.2W
被引数:
5.7W
机构
引用论文
ilastik: interactive machine learning for (bio) image analysisilastik: 用于 (生物) 图像分析的交互式机器学习
NATURE METHODS
IF32.1
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