Supervised contrastive pre-training models for mammography screening
Abstract Breast cancer is now the most deadly cancer worldwide. Mammography screening is the most effective method for early detection and diagnosis of breast cancer. Due to the lack of labeled mammograms, building an AI system for mammography screening often relies heavily on human-designed data au...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-02-01
|
| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-025-01075-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract Breast cancer is now the most deadly cancer worldwide. Mammography screening is the most effective method for early detection and diagnosis of breast cancer. Due to the lack of labeled mammograms, building an AI system for mammography screening often relies heavily on human-designed data augmentation, which doesn’t always perform robustly when applied to clinical scenarios. This paper presents a novel framework of Supervised Contrastive Pre-training followed by Supervised Fine-tuning (SCP+SF) for mammography screening. Unlike the previous approaches, the proposed supervised contrastive pre-training does not need a data augmentation module. We apply the SCP+SF framework to two challenging and important mammography screening tasks for breast cancer: mammographic abnormality screening and mammographic malignancy screening. Our extensive experiments on a large-scale dataset show that the supervised contrastive pre-training (SCP) can substantially improve the final model performance compared with the traditional direct supervised training approach. Superior results of AUC and specificity/sensitivity have been achieved on two clinically significant mammographic screening tasks in comparison with previously reported State-Of-The-Art approaches. We believe this work is the first to show that supervised contrastive pre-training (SCP) followed by supervised fine-tuning (SF) can outperform the supervised counterpart on these two critical medical imaging tasks. |
|---|---|
| ISSN: | 2196-1115 |