文章：

深度学习在人类乳腺组织组织学图像中纤维腺体密度分类的应用研究

A Deep Learning Approach for the Classification of Fibroglandular Breast Density in Histology Images of Human Breast Tissue

原文发布日期：28 January 2025

DOI: 10.3390/cancers17030449

类型: Article

开放获取: 是

英文摘要：

Background: To progress research into the biological mechanisms that link mammographic breast density to breast cancer risk, fibroglandular breast density can be used as a surrogate measure. This study aimed to develop a computational tool to classify fibroglandular breast density in hematoxylin and eosin (H&E)-stained breast tissue sections using deep learning approaches that would assist future mammographic density research. Methods: Four different architectural configurations of transferred MobileNet-v2 convolutional neural networks (CNNs) and four different models of vision transformers were developed and trained on a database of H&E-stained normal human breast tissue sections (965 tissue blocks from 93 patients) that had been manually classified into one of five fibroglandular density classes, with class 1 being very low fibroglandular density and class 5 being very high fibroglandular density. Results: The MobileNet-Arc 1 and ViT model 1 achieved the highest overall F1 scores of 0.93 and 0.94, respectively. Both models exhibited the lowest false positive rate and highest true positive rate in class 5, while the most challenging classification was class 3, where images from classes 2 and 4 were mistakenly classified as class 3. The area under the curves (AUCs) for all classes were higher than 0.98. Conclusions: Both the ViT and MobileNet models showed promising performance in the accurate classification of H&E-stained tissue sections across all five fibroglandular density classes, providing a rapid and easy-to-use computational tool for breast density analysis.

摘要翻译： 

背景：为推进乳腺密度与乳腺癌风险关联的生物学机制研究，可采用纤维腺体密度作为替代性测量指标。本研究旨在开发一种基于深度学习的计算工具，用于对苏木精-伊红（H&E）染色的乳腺组织切片进行纤维腺体密度分类，以辅助未来乳腺密度研究。方法：基于已人工划分为五个纤维腺体密度等级（1级为极低密度，5级为极高密度）的H&E染色正常人类乳腺组织切片数据库（来自93例患者的965个组织块），我们开发并训练了四种不同架构的迁移MobileNet-v2卷积神经网络（CNN）模型及四种不同架构的视觉Transformer模型。结果：MobileNet-Arc 1模型与ViT模型1分别取得最高总体F1分数0.93和0.94。两类模型在5级密度分类中均表现出最低假阳性率与最高真阳性率，而最具挑战性的分类任务出现在3级密度，存在2级与4级图像被误判为3级的情况。所有密度等级的曲线下面积（AUC）均高于0.98。结论：ViT与MobileNet模型在H&E染色组织切片的五级纤维腺体密度分类中均展现出优异性能，为乳腺密度分析提供了快速易用的计算工具。

原文链接：

A Deep Learning Approach for the Classification of Fibroglandular Breast Density in Histology Images of Human Breast Tissue