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Step-Growth Polymerization: Overview01:03

Step-Growth Polymerization: Overview

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Step-growth or condensation polymerization is a stepwise reaction of bi or multifunctional monomers to form long-chain polymers. As all the monomers are reactive, most of the monomers are consumed at the early stages of the reaction to form small chains of reactive oligomers, which then combine to form long polymer chains in the late stages. Hence, the reaction has to proceed for a long time to achieve high molecular weight polymers.
Many natural and synthetic polymers are produced by...
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相关实验视频

Updated: Jan 8, 2026

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
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PRISM:以往规范化的代自蒸,为多体细分提供动力.

Tugberk Erol1, Tuba Caglikantar2, Duygu Sarikaya3

  • 1Graduate School of Natural and Applied Sciences Gazi University Ankara Turkey.

Healthcare technology letters
|December 18, 2025
PubMed
概括
此摘要是机器生成的。

PRISM是一种用于结肠多片细分的新方法,可以在没有额外的计算成本的情况下增强深度学习模型. 这项技术提高了使用医学成像检测结直肠癌的诊断准确度.

关键词:
卷积网络是一种卷积网络.医疗图像细分 医疗图像细分聚合物细分的聚合物细分规范化 规范化 规范化自己蒸的自蒸.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 结肠直肠癌是一个重大的健康问题,早期发现对有效治疗至关重要.
  • 在医学图像中精确细分结肠多,对于早期诊断至关重要.
  • 目前用于聚细分的深度学习方法需要大量的数据和计算资源,这限制了它们的临床适用性.

研究的目的:

  • 引入PRISM,一种基于动量的新型自蒸方法,用于增强结肠多片细分.
  • 在不增加推理成本的情况下提高细分性能.
  • 开发一种可通用的解决方案,用于在不同的临床环境中对多片进行细分.

主要方法:

  • PRISM采用基于势头的自蒸方法.
  • 使用指数移动平均值 (EMA) 在训练期间在模型重量上创建了一个暂时光滑的教师模型.
  • 基于EMA的教师提供稳定,适应性的监督信号,与学生模型共同发展.

主要成果:

  • 在来自五个医疗中心的结肠镜数据集上,PRISM 获得了 0.81 的 Dice 评分和 0.75 的 IoU.
  • 该方法与基线和传统自蒸技术相比,表现优越.
  • 在独立数据集上的验证证实了PRISM的概括能力.

结论:

  • PRISM 提供了一个计算效率高和可通用的解决方案,用于结肠多片细分.
  • 基于EMA的教师模型有效地提高了细分精度.
  • 这种方法有望改善结直肠癌的早期检测和治疗.