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Abstract：
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：
癌症诊断和分类对于有效的患者管理和治疗计划至关重要。在这项研究中,提出了一种利用集成深度学习技术分析乳腺癌组织病理学图像的综合方法。我们的数据集基于来自不同中心的两个广泛使用的数据集，用于两个不同的任务：BACH和BreakHis。在BACH数据集中，采用了拟议的合奏策略，结合VGG16和ResNet50架构实现乳腺癌组织病理学图像的精确分类。引入一种新颖的图像修补技术来预处理高分辨率图像，有助于对局部感兴趣区域进行集中分析。带注释的BACH数据集包含四个不同类别的400个WSI：正常，良性,原位癌,和浸润性癌。此外,拟议的集合被用于BreakHis数据集，利用VGG16，ResNet34和ResNet50模型将显微图像分为八个不同的类别（四个良性和四个恶性）。对于这两个数据集，采用5倍交叉验证方法进行严格的培训和测试.初步实验结果表明，斑块分类准确率为95.31%（对于BACH数据集），WSI图像分类准确率为98.43%（BreakHis）。这项研究为利用人工智能推进乳腺癌诊断的持续努力做出了重大贡献，可能促进改善患者预后并减轻医疗负担。
