双通道卷积神经网络与挤压和兴奋注意力用于肺部和结肠组织病理学分类
1Computer and Information Technology Department, Jubail Industrial College, P.O. Box 10099, Jubail Industrial City 31961, Saudi Arabia.
Journal of imaging
|December 24, 2025
概括
一个新的双路径卷积神经网络,DPCSE-Net,使用可解释的AI准确地分类肺癌和结肠癌. 这种自动化系统以低复杂度实现高性能,有助于组织病理学诊断.
科学领域:
- * 计算机病理学
- * 在瘤学中的人工智能
- * 医学图像分析
背景情况:
- *肺癌和结肠癌是导致死亡的主要原因,需要精确的组织病理学诊断.
- *手动的幻灯片检查是耗时的,并且受观察者之间的变化影响.
- *开发自动化,可解释的诊断系统对于提高准确性和效率至关重要.
研究的目的:
- * 引入DPCSE-Net,一个轻量级的双路径卷积神经网络,具有挤压激发 (SE) 注意力机制.
- * 评估DPCSE-Net在分类肺癌和结肠癌组织病理学方面的表现.
- * 通过使用像Grad-CAM和Integrated Gradients这样的可视化技术来提高模型的解释性.
主要方法:
- * 开发了一种双路径卷积神经网络 (DPCSE-Net),集成多尺度特征提取和SE注意力.
- *使用梯度加权类激活映射 (Grad-CAM),注意热图和集成梯度来实现模型的可解释性.
- * 在肺癌和结肠癌分类的LC25000数据集上验证了DPCSE-Net.
主要成果:
- * DPCSE-Net在LC25000数据集上实现了最先进的准确性 (99.88%) 和F1得分.
- * 该模型证明了高效率与低计算复杂性.
- * 废弃性研究证实了双路设计和SE模块的有效性.
- * 定性分析表明,该模型的重点与诊断相关区域保持一致.
结论:
- * DPCSE-Net提供了一个准确,高效和可解释的计算机辅助组织病理诊断框架.
- *这项研究支持将可解释的AI整合到用于癌症检测的医学图像分析中.
- * DPCSE-Net有可能帮助病理学家更快,更可靠地进行诊断.
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