文章：

基于集成深度学习的全切片图像组织病理学乳腺癌亚型与侵袭性诊断图像分类

Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology

原文发布日期：14 June 2024

DOI: 10.3390/cancers16122222

类型: Article

开放获取: 是

英文摘要：

Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens.

摘要翻译： 

癌症诊断与分类对于有效的患者管理与治疗规划至关重要。本研究提出了一种综合方法，利用集成深度学习技术分析乳腺癌组织病理学图像。我们的数据集基于来自不同中心、针对两项不同任务广泛使用的两个数据集：BACH和BreakHis。在BACH数据集中，我们采用了一种集成策略，结合VGG16和ResNet50架构，以实现对乳腺癌组织病理学图像的精确分类。通过引入新颖的图像分块技术对高分辨率图像进行预处理，有助于对局部感兴趣区域进行聚焦分析。标注后的BACH数据集包含400张全切片图像，涵盖四个不同类别：正常组织、良性病变、原位癌和浸润性癌。此外，所提出的集成方法也应用于BreakHis数据集，利用VGG16、ResNet34和ResNet50模型将显微图像分为八个不同类别（四种良性和四种恶性）。针对两个数据集均采用五折交叉验证方法进行严格训练与测试。初步实验结果显示，在BACH数据集上获得95.31%的切片分类准确率，在BreakHis数据集上获得98.43%的全切片图像分类准确率。这项研究为利用人工智能推进乳腺癌诊断的持续努力作出了重要贡献，有望改善患者预后并减轻医疗负担。

原文链接：

Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology