Fiaz Ahsan, Raza Basit, Faheem Muhammad, Raza Aadil
Department of Computer Science COMSATS University Islamabad (CUI) Islamabad Pakistan.
School of Technology and Innovations University of Vaasa Vaasa Finland.
Healthc Technol Lett. 2024 Oct 23;11(6):471-484. doi: 10.1049/htl2.12093. eCollection 2024 Dec.
Breast cancer is one of the most common causes of death in women in the modern world. Cancerous tissue detection in histopathological images relies on complex features related to tissue structure and staining properties. Convolutional neural network (CNN) models like ResNet50, Inception-V1, and VGG-16, while useful in many applications, cannot capture the patterns of cell layers and staining properties. Most previous approaches, such as stain normalization and instance-based vision transformers, either miss important features or do not process the whole image effectively. Therefore, a deep fusion-based vision Transformer model (DFViT) that combines CNNs and transformers for better feature extraction is proposed. DFViT captures local and global patterns more effectively by fusing RGB and stain-normalized images. Trained and tested on several datasets, such as BreakHis, breast cancer histology (BACH), and UCSC cancer genomics (UC), the results demonstrate outstanding accuracy, F1 score, precision, and recall, setting a new milestone in histopathological image analysis for diagnosing breast cancer.
乳腺癌是现代世界女性最常见的死亡原因之一。组织病理学图像中的癌组织检测依赖于与组织结构和染色特性相关的复杂特征。像ResNet50、Inception-V1和VGG-16这样的卷积神经网络（CNN）模型虽然在许多应用中很有用，但无法捕捉细胞层模式和染色特性。以前的大多数方法，如染色归一化和基于实例的视觉Transformer，要么遗漏重要特征，要么不能有效地处理整个图像。因此，提出了一种基于深度融合的视觉Transformer模型（DFViT），该模型结合了CNN和Transformer以进行更好的特征提取。DFViT通过融合RGB图像和染色归一化图像更有效地捕捉局部和全局模式。在多个数据集上进行训练和测试，如BreakHis、乳腺癌组织学（BACH）和加州大学圣克鲁兹分校癌症基因组学（UC），结果显示出卓越的准确率、F1分数、精确率和召回率，为乳腺癌诊断的组织病理学图像分析树立了新的里程碑。
