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使用人工智能对莫斯手术部位进行弱监督分类.

Daan J Geijs1, Lisa M Hillen2, Stephan Dooper1

  • 1Department of Pathology, Research Institute for Medical Innovation and Oncode Institute, Radboud University Medical Center, Nijmegen, The Netherlands.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
|November 10, 2024
PubMed
概括
此摘要是机器生成的。

一个新的深度学习模型在莫斯微图外科手术 (MMS) 图像中准确检测基底细胞癌 (BCC). 这种人工智能工具可以提高皮肤癌治疗的诊断准确性和解释性.

关键词:
莫斯的微图外科手术.基底细胞癌的癌症.计算病理学计算病理学深度学习是一种深度学习.皮肤病理学 皮肤病理学数字病理学数字病理学

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科学领域:

  • 皮肤病学 皮肤病学
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 基底细胞癌 (BCC) 的发病率正在上升,增加了医疗保健负担.
  • 对于莫斯微图手术 (MMS) 的组织病理学诊断对于BCC治疗至关重要.
  • 在MMS全幻灯片图像中准确检测BCC存在诊断挑战.

研究的目的:

  • 开发和评估用于在MMS图像中检测BCC的深度学习模型.
  • 将弱监督学习与使用注意力地图的可解释细分结合起来.
  • 为了提高BCC识别的诊断准确性和可解释性.

主要方法:

  • 开发了一个深度学习模型,结合了弱监督学习和基于注意力的细分.
  • 利用来自两个医疗中心的数据集进行培训和内部测试.
  • 在没有微调的情况下,在独立的外部数据集上验证了模型.

主要成果:

  • 在内部测试中达到0.958的平均AUC,在外部数据集中达到0.934.
  • 注意地图为模型决策提供了洞察力,并突出了关键区域.
  • 瘤局部化灵敏度为0.853,平均每片8个假阳性,在过小检测时减少到每片2个假阳性,灵敏度为0.873.

结论:

  • 深度学习模型在检测MMS图像中的BCC方面表现出高的有效性和稳定性.
  • 注意地图提高了模型的解释性,帮助皮肤病理学家和外科医生.
  • 这种人工智能工具对改善BCC诊断和MMS程序充满希望.