Summary of Supervised Contrastive Vision Transformer For Breast Histopathological Image Classification, by Mohammad Shiri et al.
Supervised Contrastive Vision Transformer for Breast Histopathological Image Classification
by Mohammad Shiri, Monalika Padma Reddy, Jiangwen Sun
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Supervised Contrastive Vision Transformer (SupCon-ViT) is a novel approach that leverages the strengths of transfer learning and supervised contrastive learning to improve the classification accuracy and generalization of invasive ductal carcinoma (IDC) in breast cancer diagnosis. By leveraging pre-trained vision transformers and supervised contrastive learning, SupCon-ViT achieves state-of-the-art performance on a benchmark breast cancer dataset with an F1-score of 0.8188, precision of 0.7692, and specificity of 0.8971, outperforming existing methods. The proposed model also demonstrates resilience in scenarios with minimal labeled data, making it highly efficient in real-world clinical settings where labelled data is limited. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to look at breast cancer pictures using artificial intelligence. It uses special computer vision tools that learn from lots of old pictures and then use what it learns to help doctors diagnose breast cancer better. The new method, called SupCon-ViT, does a great job of telling the difference between different types of breast cancer. It even works well when there aren’t many examples to look at, which is important in real-life hospitals where not all patients have lots of pictures taken. |
Keywords
» Artificial intelligence » Classification » F1 score » Generalization » Precision » Supervised » Transfer learning » Vision transformer » Vit
