Image-Based Breast Implant Detection for Mammography Dataset Curation and Near-Real-Time Deployment: Comparing Foundation Models and Task-Specific Convolutional Models
eess.IV, cs.CV, q-bio.TO
Submitted: 2026-10-01
Updated: 2026-10-01
Code: https://github.com/XiaoweiXu/ImageBR
Terminology
Sources
- Radio-opaque artefacts in digital mammography: automatic detection and analysis of downstream effects
- Deep Residual Learning for Image Recognition
- On the Opportunities and Risks of Foundation Models
- Bias and Generalizability of Foundation Models across Datasets in Breast Mammography
- Exploring scalable medical image encoders beyond text supervision
- Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in Mammography
- Adam: A Method for Stochastic Optimization
- MammoDINO: Anatomically Aware Self-Supervision for Mammographic Images
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
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