  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.
