Invasive ductal carcinoma (IDC) is the most prevalent form of breast cancer.
Breast tissue histopathological examination is critical in diagnosing and
classifying breast cancer. Although existing methods have shown promising
results, there is still room for improvement in the classification accuracy and
generalization of IDC using histopathology images. We present a novel approach,
Supervised Contrastive Vision Transformer (SupCon-ViT), for improving the
classification of invasive ductal carcinoma in terms of accuracy and
generalization by leveraging the inherent strengths and advantages of both
transfer learning, i.e., pre-trained vision transformer, and supervised
contrastive learning. Our results on a benchmark breast cancer dataset
demonstrate that SupCon-Vit achieves state-of-the-art performance in IDC
classification, with an F1-score of 0.8188, precision of 0.7692, and
specificity of 0.8971, outperforming existing methods. In addition, the
proposed model demonstrates resilience in scenarios with minimal labeled data,
making it highly efficient in real-world clinical settings where labelled data
is limited. Our findings suggest that supervised contrastive learning in
conjunction with pre-trained vision transformers appears to be a viable
strategy for an accurate classification of IDC, thus paving the way for a more
efficient and reliable diagnosis of breast cancer through histopathological
image analysis.

使用监督对比视觉转换器（SupCon-ViT）的新方法，借助迁移学习和监督对比学习的优势，改进了浸润性导管癌（IDC）的分类准确性和泛化能力，并在基准乳腺癌数据集上取得了最先进的性能，F1 分数为 0.8188，精确度为 0.7692，特异度为 0.8971，超过现有的方法。该模型在有限标记数据的场景下表现出韧性，因此在标记数据有限的真实临床环境中具有高效性，研究结果表明，监督对比学习结合预训练的视觉转换器似乎是进行 IDC 准确分类的可行策略，从而为通过组织病理学图像分析更高效可靠的乳腺癌诊断铺平了道路。