使用局部窗口视觉变压器进行跨平台多种癌症组织病理学分类
Md Darun Nayeem1, Nusrat Jahan Nisita1, Md Masudul Islam2
1Bangladesh University of Business and Technology, Mirpur, Dhaka, Bangladesh.
Scientific reports
|November 19, 2025
概括
CancerDet-Net使用人工智能准确地分类了9种癌症亚型,分为4种主要类型. 这种先进的深度学习模型通过可解释的AI提供实时临床使用,改善癌症诊断.
科学领域:
- 数字病理学数字病理学
- 在瘤学中使用人工智能
- 计算生物学 计算生物学
背景情况:
- 准确的组织病理图像分类对于癌症诊断和治疗至关重要.
- 当前的深度学习模型往往缺乏临床应用的概括性和透明度.
- 现有的模型通常仅限于单一癌症分类.
研究的目的:
- 开发一个统一的深度学习框架,CancerDet-Net,用于多种癌症的基因病理图像分类.
- 为了提高模型的概括性,解释性和临床适用性.
- 为了在分类各种癌症亚型时实现高精度.
主要方法:
- 可分离的卷积层的集成,视觉变压器 (ViT) 块具有局部窗口稀疏的自我注意力.
- 通过交叉尺度特征 (CSF) 融合结合的分层多尺度门式注意力机制 (HMSGA) 的应用.
- 开发可解释的人工智能 (XAI) 可视化,以实现模型透明度.
主要成果:
- 在四种主要癌症类型中,CancerDet-Net在分类9种组织病理学亚型时实现了98.51%的最高性能准确度.
- 该模型在数据集中显示出强大的通用性.
- 集成的XAI可视化为临床解释提供了透明度.
结论:
- CancerDet-Net为数字病理学中的多种癌症分类提供了一个全面和统一的框架.
- 该模型的高精度,可概括性和可解释性代表了显著的进步.
- 通过网络和移动平台的部署准备有助于实时临床使用,增强AI驱动的癌症诊断.
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