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对于病理学的深度学习:YOLOv8与 EigenCAM 可用于可靠的结肠直肠癌诊断

Mohamed Farsi¹, Hanaa ZainEldin², Hanaa A Sayed^3,4

¹Department of Information Systems, College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia.

Bioengineering (Basel, Switzerland)

|November 27, 2025

概括

这项研究引入了一种深度学习框架,用于使用YOLOv8和EigenCAM诊断结直肠癌 (CRC). 人工智能模型实现了高精度,为病理学家提供了可靠的工具.

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
用于医学成像的深度学习

背景情况:

结肠直肠癌 (CRC) 是全球癌症死亡的主要原因.
准确和及时的诊断对于有效的CRC治疗至关重要.
目前的诊断方法,如手动组织病理学,面临着观察者的变化,而计算工具往往缺乏解释性.

研究的目的:

开发和验证一个深度学习框架,用于准确和可解释的结直肠癌病变分类.
将YOLOv8架构与EigenCAM集成在一起,以在组织病理学中提供透明的AI解释.
为人工智能辅助的CRC诊断建立一个临床可靠的基础.

主要方法:

获得和预处理了5000张染色H&E结直肠组织幻灯片的数据集.
五种YOLOv8变异被对多类病变分类进行了比较评估.
EigenCAM用于可视化歧视性区域,提高模型的可解释性.
统计验证方法包括布兰德-阿尔特曼地块和CDF被用于评估稳定性.

主要成果:

该YOLOv8 XLarge模型实现了99.38%的训练准确率和96.62%的测试准确率.
与现有的基于CNN和变压器的系统相比,该框架表现出了卓越的性能.
EigenCAM可视化成功突出了驱动AI预测的关键区域.
广泛的统计验证证实了该框架的可靠性和稳定性.

结论:

开发的深度学习框架为人工智能辅助结直肠癌诊断提供了精确和可解释的解决方案.
这种方法解决了手动评估和当前计算方法的局限性,通过将高精度与视觉解释相结合.
该框架代表了AI在病理学工作流程中的临床部署,以改善CRC检测的重大进展.

关键词:

癌症的诊断癌症的诊断深度学习 (DL) 是指深度学习.可解释的人工智能 (XAI)

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