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Interval and large invasive breast cancers, which are associated with worseprognosis than other cancers, are usually detected at a late stage due to falsenegative assessments of screening mammograms. The missed screening-timedetection is commonly caused by the tumor being obscured by its surroundingbreast tissues, a phenomenon called masking. To study and benchmarkmammographic masking of cancer, in this work we introduce CSAW-M, the largestpublic mammographic dataset, collected from over 10,000 individuals andannotated with potential masking. In contrast to the previous approaches whichmeasure breast image density as a proxy, our dataset directly providesannotations of masking potential assessments from five specialists. We alsotrained deep learning models on CSAW-M to estimate the masking level and showedthat the estimated masking is significantly more predictive of screeningparticipants diagnosed with interval and large invasive cancers -- withoutbeing explicitly trained for these tasks -- than its breast densitycounterparts.

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