在结直肠癌中的瘤芽分类使用基于注意力的深度多重实例学习和特定领域的基础模型
Mesut Şeker1, M Khalid Khan Niazi2, Wei Chen2
1Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir 21280, Turkey.
Cancers
|April 14, 2025
概括
这项研究引入了一种自动化的深度学习系统,用于分类结直肠癌中的瘤芽,提高预后准确度. 开发的模型在识别转移的早期迹象方面表现出很高的性能.
科学领域:
- 计算病理学计算病理学
- 人工智能在瘤学中的应用
- 数字病理学数字病理学
背景情况:
- 在结直肠癌 (CRC) 中,瘤芽 (TB) 是与转移有关的关键预后指标.
- 手动结核病评估是主观和具有挑战性的,特别是在高放大时,导致诊断不一致.
- 需要自动化方法来提高结核病识别的准确性和可重复性.
研究的目的:
- 开发和评估一个自动化的深度学习系统,用于准确的瘤芽分类在结肠直肠癌.
- 为了比较各种基础模型在结核病检测中的特征提取的性能.
- 通过改进结核病识别,提高结肠直肠癌的预后评估.
主要方法:
- 一个深度学习模型被训练使用弱监督学习在整个幻灯片图像 (WSIs) 从瘤入侵前面.
- 评估了多个基础模型的特征提取,并比较了性能.
- 来自基于注意力的多实例学习 (ABMIL) 的注意力热图被分析为可解释性和与TBs的对齐.
主要成果:
- 在交叉验证中,Phikon-v2获得了最高的平均AUC (0.984) 和回忆 (0.947).
- 在外部测试组中,Phikon-v2表现出卓越的性能,AUC为0.979和精度为0.980.
- 该模型的注意力机制为结核病识别提供了可解释的见解.
结论:
- 拟议的自动化系统显著提高了结直肠癌中瘤芽评估的准确性.
- 这种深度学习方法为改善CRC的预后评估提供了一个有希望的工具.
- 该技术在其他癌症类型的结核病评估中具有潜在的应用.
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