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GLAM:使用适应的小鼠模型对人类病理性病变进行淋巴细胞细分.

Lining Yu1, Mengmeng Yin2, Ruining Deng1

  • 1Department of Computer Science, Vanderbilt University, Nashville, TN, USA.

Proceedings of SPIE--the International Society for Optical Engineering
|December 1, 2025
PubMed
概括
此摘要是机器生成的。

一个新的深度学习模型,GLAM,表明混合学习有效地将病理性淋巴细胞细分从小鼠模型转移到人类患者身上. 这种方法改善了临床实践中病变的分析.

关键词:
球球损伤的发生.它们是质球体.细分化 细分化的细分化转让杆是一种转让杆.整个幻灯片图像的图像.

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科学领域:

  • 科和计算病理学
  • 生物医学图像分析
  • 翻译研究是翻译研究.

背景情况:

  • 精确的脏组织测量对于药物开发和了解疾病至关重要.
  • 现有的淋巴细胞细分技术对从小鼠转化到人体的转化有前途,但往往忽视了复杂的病理病变.
  • 病态球体在临床上更重要,动物模型的数据可以很容易地缩放.

研究的目的:

  • 研究在小鼠模型上训练的病理细分模型对人类患者的有效性.
  • 介绍GLAM,一种深度学习方法,用于使用小鼠模型细粒度细分人类病变.
  • 评估零射击和混合学习策略,用于在病变细分中从老鼠转移到人类的学习.

主要方法:

  • 开发了GLAM,这是一个深度学习框架,用于对人类病变进行细分.
  • 用于训练细分算法的小鼠模型.
  • 评估了零射击转移学习和混合学习策略,利用小鼠样本进行人类数据细分.

主要成果:

  • 与其他策略相比,混合学习在对人类病理性病变进行细分方面表现优越.
  • 这项研究成功地解决了复杂的病变细分的老鼠对人类转移学习的挑战.
  • GLAM框架促进了人类病变的细粒度细分.

结论:

  • 混合学习是将病变细分模型从小鼠转移到人类的高效策略.
  • 这些发现支持在动物数据上训练的深度学习模型的临床实用性,用于人类病理分析.
  • 这项研究推动了人工智能在科中的应用,以改善诊断和治疗开发.