使用DS-EffNet在组织病理学图像中基于深度学习的肺腺癌亚型的分类使用DS-EffNet
Peihe Jiang1, Weilong Chen1, Xiaogang Song2
1School of Physics and Electronic Information, Yantai University, Yantai, 264005, China.
Human pathology
|December 20, 2025
概括
这项研究引入了一种高效的深度学习模型,用于从组织病理图像中分类肺腺癌 (LADC) 亚型,实现高精度和概括性,以改善诊断和治疗规划.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 计算机科学 计算机科学
背景情况:
- 肺腺癌 (LADC) 亚型分类对于理解疾病异质性和指导治疗至关重要.
- 组织病理图像分析由于复杂性和异质性而存在挑战.
研究的目的:
- 开发一个高效的深度学习模型,用于准确的LADC亚型分类.
- 为了增强特征提取和复杂的病理模式的建模在他的病理图像.
主要方法:
- 将深度可分离的剩余块 (DSResBlock),RefConv,频道注意力聚合 (CAP) 和多维协作注意力 (MCA) 模块集成到EfficientNetV2-S.中.
- 为优化特征提取和模式建模开发DS-EffNet模型.
主要成果:
- 在初级数据集上,DS-EffNet模型实现了95.1%的准确性,0.938的F1得分和0.994的AUC.
- 在LC25000数据集上实现了100%的概括准确性,证明了跨机构的性能.
- 废弃研究证实了每个模块的协同贡献,特别是复杂特征的MCA和计算效率的RefConv.
结论:
- 拟议的DS-EffNet模型为LADC亚型分类提供了一种新且高效的方法.
- 该模型的性能表明,它有可能成为帮助病理学家快速传递亚型信息的工具.
- 这项研究为医疗图像分类提供了一个新的设计范式,适用于其他组织学任务,并有助于治疗分层.
相关概念视频
01:22Classification of Epithelial Tissues: Overview
Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
Based on the number of cell layers,...
Based on the number of cell layers,...
01:29Classification of Epithelial Tissues: Stratified Epithelium
Stratified epithelium consists of several stacked layers of cells. They provide the durability to withstand constant physical and chemical attacks. Stratified epithelium is named after the shape of the most apical layer of cells. Stratified squamous epithelium is the most common type found in the human body. In this tissue, the apical cells are squamous, whereas the basal layer contains either columnar or cuboidal cells. The basal cells divide to form new daughter cells, which gradually become...