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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
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Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
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使用监督和深度学习在清细胞肉瘤和黑色素瘤之间进行组织学区分.

Jakob M T Moran1, Ivan Chebib1, Mark Sabbagh1

  • 1Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

APMIS : acta pathologica, microbiologica, et immunologica Scandinavica
|March 10, 2026
PubMed
概括

机器学习模型使用核特征有效地区分透明细胞肉瘤和黑色素瘤. 这些人工智能分类器显示出高准确度,在分子测试不可用时有助于诊断.

关键词:
分类器分类器是分类器.透明细胞肉瘤是什么?深度学习是一种深度学习.黑色素瘤是一种黑色素瘤.形态测量法 形态测量法 形态测量法核周围区域的核周围区域

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科学领域:

  • 计算病理学计算病理学
  • 数字病理学数字病理学
  • 机器学习在瘤学中的应用

背景情况:

  • 清细胞肉瘤和黑色素瘤共享组织学和免疫类型的相似性,使诊断复杂化.
  • 对EWSR1重组的分子测试至关重要,但并不总是可用于区分这些瘤.

研究的目的:

  • 开发和验证基于机器学习的分类器,以区分清细胞肉瘤和黑色素瘤.
  • 探索核形态测量和深度学习在诊断病理学的实用性.

主要方法:

  • 从透明细胞肉瘤和黑色素瘤中以血素和素染色的数字化幻灯片.
  • 构建了核形态测量和深度学习分类器 (CLAM/ResNet-50,CTransPath,UNI).
  • 在独立的外部验证集上评估分类器性能.

主要成果:

  • 两个核形态分类器在外部验证中实现了80%-90%的准确性.
  • 最优的深度学习分类器 (CLAM/CTransPath) 的准确率达到90%.
  • 他们的表现与经验丰富的病理学家的表现相当.

结论:

  • 可解释的核形态测量和深度学习分类器可以可靠地区分清细胞肉瘤和黑色素瘤.
  • 使用机器学习进行定量形态测量分析可以作为一种有价值的诊断辅助.
  • 这些人工智能工具可以提高诊断准确度,特别是当分子测试有限时.