# Radio-opaque artefacts in digital mammography: automatic detection and analysis of downstream effects

arXiv: https://arxiv.org/abs/2410.03809 · HTML: https://mlsanity.com/paper/2410.03809/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 1 of 1)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)

## Built on by (most cited first, 4 of 4)

- [Diffusion Counterfactual Generation with Semantic Abduction](https://mlsanity.com/paper/2506.07883/) (2025)
- [PromptGate Client Adaptive Vision Language Gating for Open Set Federated Active Learning](https://mlsanity.com/paper/2603.07163/) (2026)
- [FIDAL: Diversity-Aware Federated Active Learning Under Real-World Distribution Shifts](https://mlsanity.com/paper/2609.31637/) (2026)
- [Image-Based Breast Implant Detection for Mammography Dataset Curation and Near-Real-Time Deployment: Comparing Foundation Models and Task-Specific Convolutional Models](https://mlsanity.com/paper/2610.03817/) (2026)

## Abstract (arXiv)

This study investigates the effects of radio-opaque artefacts, such as skin markers, breast implants, and pacemakers, on mammography classification models. After manually annotating 22,012 mammograms from the publicly available EMBED dataset, a robust multi-label artefact detector was developed to identify five distinct artefact types (circular and triangular skin markers, breast implants, support devices and spot compression structures). Subsequent experiments on two clinically relevant tasks - breast density assessment and cancer screening - revealed that these artefacts can significantly affect model performance, alter classification thresholds, and distort output distributions. These findings underscore the importance of accurate automatic artefact detection for developing reliable and robust classification models in digital mammography. To facilitate future research our annotations, code, and model predictions are made publicly available.