Abstract
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.
Abstract (translated)
侵袭性导管癌（IDC）是乳腺癌中最常见的类型。对乳房组织进行病理学检查对于诊断和分类乳腺癌至关重要。尽管现有的方法已经显示出良好的效果，但在使用组织学图像进行IDC分类的准确性和泛化方面仍有改进的空间。我们提出了一个新方法，监督对比视觉Transformer（SupCon-ViT），通过利用迁移学习（即预训练视觉Transformer）的固有优势和监督对比学习（Supervised contrastive learning）的优势，提高IDC分类的准确性和泛化。我们在基准乳腺癌数据集上的结果表明，SupCon-ViT在IDC分类上实现了最先进的性能，其F1分数为0.8188，精确度为0.7692，特异性为0.8971，优于现有方法。此外，与标记数据较少的情景相对，所提出的模型表现出强大的鲁棒性，因此在临床实践中，具有有限标记数据的情况下，该模型具有很高的效率。我们的研究结果表明，在监督对比学习与预训练视觉Transformer相结合的情况下，准确地分类IDC可能是可行的策略，为通过组织学图像分析更准确和可靠的乳腺癌诊断铺平道路。
URL
https://arxiv.org/abs/2404.11052
