Supervised Contrastive Vision Transformer for Breast Histopathological Image Classification
CoRR(2024)
摘要
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.
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