Summary of Mammo-clip: Leveraging Contrastive Language-image Pre-training (clip) For Enhanced Breast Cancer Diagnosis with Multi-view Mammography, by Xuxin Chen et al.
Mammo-CLIP: Leveraging Contrastive Language-Image Pre-training (CLIP) for Enhanced Breast Cancer Diagnosis with Multi-view Mammography
by Xuxin Chen, Yuheng Li, Mingzhe Hu, Ella Salari, Xiaoqian Chen, Richard L.J. Qiu, Bin Zheng, Xiaofeng Yang
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 A new approach to computer-aided diagnosis (CAD) in mammograms uses Contrastive Language-Image Pre-training (CLIP) to fuse features from multiple views. The proposed Mammo-CLIP framework processes four mammogram views and corresponding texts, achieving state-of-the-art results on two datasets for breast cancer detection. By fine-tuning a parameter-dense model with limited samples and computational resources, Mammo-CLIP outperforms existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help doctors detect breast cancer from X-ray images uses artificial intelligence. The method, called Mammo-CLIP, combines information from different views of the breasts and text descriptions of the images. This helps improve accuracy in diagnosing breast cancer. Researchers tested Mammo-CLIP on two groups of images and found it performed better than other methods. |
Keywords
» Artificial intelligence » Fine tuning
