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.
@article{arxiv.2112.01330,
title = {CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer},
author = {Moein Sorkhei and Yue Liu and Hossein Azizpour and Edward Azavedo and Karin Dembrower and Dimitra Ntoula and Athanasios Zouzos and Fredrik Strand and Kevin Smith},
journal= {arXiv preprint arXiv:2112.01330},
year = {2025}
}
Comments
35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks