论文标题
vit-deit：乳腺癌组织病理学图像分类的合奏模型
ViT-DeiT: An Ensemble Model for Breast Cancer Histopathological Images Classification
论文作者
论文摘要
乳腺癌是世界上最常见的癌症，也是导致女性死亡的第二常见癌症。使用组织病理学图像对乳腺癌的及时诊断对于患者护理和治疗至关重要。病理学家可以在基于图像处理的新方法的帮助下进行更准确的诊断。这种方法是两种类型的预训练视觉变压器模型的集合模型，即视觉变压器和数据有效的图像变压器。拟议的整体模型将乳腺癌组织病理学图像分为八个类，其中四个分为良性，而其他分类为良性。使用公共数据集评估提出的模型。实验结果显示98.17％的精度，98.18％的精度，98.08％的召回率和98.12％的F1得分。
Breast cancer is the most common cancer in the world and the second most common type of cancer that causes death in women. The timely and accurate diagnosis of breast cancer using histopathological images is crucial for patient care and treatment. Pathologists can make more accurate diagnoses with the help of a novel approach based on image processing. This approach is an ensemble model of two types of pre-trained vision transformer models, namely, Vision Transformer and Data-Efficient Image Transformer. The proposed ensemble model classifies breast cancer histopathology images into eight classes, four of which are categorized as benign, whereas the others are categorized as malignant. A public dataset was used to evaluate the proposed model. The experimental results showed 98.17% accuracy, 98.18% precision, 98.08% recall, and a 98.12% F1 score.
