基于深度学习的 Pleural 恶性瘤的分类,使用医学胸腔镜图像
Yu Jin Hong1, Se Hee Ha2, Seong Hyeon Park2
1Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, New Korea Hospital, Gimpo, Republic of Korea.
Translational lung cancer research
|November 14, 2025
概括
这项研究引入了一种深度学习模型,用于从胸腔镜图像中分类胸腔恶性瘤,从而实现高精度. 人工智能工具有助于在医疗胸腔镜检查期间诊断恶性多流动.
科学领域:
- 医学成像医学成像
- 人工智能的人工智能是人工智能.
- 在瘤学瘤学.
背景情况:
- 恶性流 (MPE) 是晚期肺癌的一个常见并发症.
- 准确诊断MPE对于有效的治疗计划至关重要.
- 医学胸腔镜 (MT) 提供直接可视化和活检,但依赖于操作人员的专业知识和病理确认.
研究的目的:
- 开发和评估一种深度学习模型,用于使用胸腔镜图像对肺部恶性瘤进行分类.
- 评估人工智能模型在识别癌症性多流的诊断性能.
主要方法:
- 一个卷积神经网络 (InceptionV3) 用426名患者的4932张胸腔镜图像进行训练.
- 在模型培训期间使用了转移学习和在线增强.
- 使用准确度,精度,回忆,F1得分和ROC-AUC来评估性能,并使用Grad-CAM进行可视化.
主要成果:
- 深度学习模型实现了81.7%的准确度,82.4%的精度,86.6%的回忆率和84.6%的F1分数.
- 在ROC曲线下的面积 (AUC) 为0.90,表明了出色的区分能力.
- 格拉德-CAM证实了该模型的重点是相关的肺异常,即使在微妙的情况下.
结论:
- 这是第一个深度学习模型,用于从MT图像中分类腹腔恶性瘤.
- 该模型表现出强大的诊断性能,可能有助于在胸腔镜检查期间实时临床决策.
- 人工智能工具可以帮助识别活检或多发性瘤检查的恶性点,改善患者管理.
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