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相关概念视频

Fixation and Sectioning01:03

Fixation and Sectioning

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Two basic types of preparation are used to visualize specimens with a light microscope: wet mounts and fixed specimens.
The simplest type of preparation is the wet mount, in which the specimen is placed in a drop of liquid on the slide. A liquid specimen can be directly deposited on the slide using a dropper. Solid specimens, such as skin scraping, can be placed on the slide before adding a drop of liquid to prepare the wet mount. Sometimes the liquid is simply water, but stains are often added...
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相关实验视频

Updated: May 15, 2025

Histological-Based Stainings Using Free-Floating Tissue Sections
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组织病理学基础模型 - 粉丝或风味.

Saghir Alfasly1, Peyman Nejat1, Sobhan Hemati1

  • 1KIMIA Lab, Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN.

Mayo Clinic proceedings. Digital health
|April 10, 2025
PubMed
概括

在基因病理学任务中,特定领域模型的表现优于一般基础模型. 专门的数据集对于培养生物医学中有效的视觉语言模型至关重要,需要专家验证.

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科学领域:

  • 组织病理学 组织病理学
  • 人工智能在医学中的应用
  • 计算机视觉 计算机视觉

背景情况:

  • 基础模型 (FMs) 在各种领域表现有前途,但它们在组织病理学中的表现需要彻底评估.
  • 在互联网数据上微调的通用FM与在精心策划的数据集上训练的专业模型进行比较.

研究的目的:

  • 为了评估目前的基础模型在组织病理学中的性能.
  • 将通用FM (CLIP衍生品,如PLIP,BiomedCLIP) 与特定域组织学模型进行比较.

主要方法:

  • 在8个数据集上评估了模型 (4个是梅奥诊所内部,4个是公共数据集:PANDA,BRACS,CAMELYON16,DigestPath).
  • 评估指标包括在整个幻灯片图像和补丁级别的准确性和宏观平均F1得分 (MV@5).
  • 所有模型评估都使用了分类任务.

主要成果:

  • 在大多数任务中,域特定模型 (DinoSSLPath,KimiaNet) 的表现优于一般的FM.
  • 在内部结直肠癌和肝脏图像检索方面,DinoSSLPath表现出色 (MV@5 F1: 63%, 74%).
  • 在乳腺和皮肤癌任务中,KimiaNet领先,在CAMELYON16 (75%) 上表现良好.

结论:

  • 专门的,特定领域的模型在组织病理学中表现出卓越的性能.
  • 高质量的多模医疗数据集与专家验证的对齐对于推动生物医学视觉语言FMs至关重要.
  • 在数据策划方面的协作努力对于临床翻译至关重要.