# CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer

arXiv: https://arxiv.org/abs/2112.01330 · HTML: https://mlsanity.com/paper/2112.01330/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 2 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [MoCo-CXR: MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models](https://mlsanity.com/paper/2010.05352/) (2020)
- [Adding Seemingly Uninformative Labels Helps in Low Data Regimes](https://mlsanity.com/paper/2008.00807/) (2020)

## Built on by (most cited first, 9 of 9)

- [Generative AI for Medical Imaging: extending the MONAI Framework](https://mlsanity.com/paper/2307.15208/) (2023)
- [PatchDropout: Economizing Vision Transformers Using Patch Dropout](https://mlsanity.com/paper/2208.07220/) (2022)
- [MedVAE: Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders](https://mlsanity.com/paper/2502.14753/) (2025)
- [A Systematic Review of NeurIPS Dataset Management Practices](https://mlsanity.com/paper/2411.00266/) (2024)
- [Optimizing Breast Cancer Detection in Mammograms: A Comprehensive Study of Transfer Learning, Resolution Reduction, and Multi-View Classification](https://mlsanity.com/paper/2503.19945/) (2025)
- [BreastGPT: A Multimodal Large Language Model for the Full Spectrum of Breast Cancer Clinical Routine](https://mlsanity.com/paper/2606.04911/) (2026)
- [Efficient Multi-scale Masked Autoencoders with Hybrid-Attention Mechanism for Breast Lesion Classification](https://mlsanity.com/paper/2503.07157/) (2025)
- [Knowledge-based anomaly detection for identifying network-induced shape artifacts](https://mlsanity.com/paper/2511.04729/) (2025)
- [MammoFlow: Multiview Mammogram Synthesis with Anatomically Consistent Flow Matching](https://mlsanity.com/paper/2606.28537/) (2026)

## Abstract (arXiv)

Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms. The missed screening-time detection is commonly caused by the tumor being obscured by its surrounding breast tissues, a phenomenon called masking. To study and benchmark mammographic masking of cancer, in this work we introduce CSAW-M, the largest public mammographic dataset, collected from over 10,000 individuals and annotated with potential masking. In contrast to the previous approaches which measure breast image density as a proxy, our dataset directly provides annotations of masking potential assessments from five specialists. We also trained deep learning models on CSAW-M to estimate the masking level and showed that the estimated masking is significantly more predictive of screening participants diagnosed with interval and large invasive cancers -- without being explicitly trained for these tasks -- than its breast density counterparts.