Yue Liu, Karin Dembrower, Athanasios Zouzos, Fredrik Strand, Kevin Smith, Hossein Azizpour, Moein Sorkhei, Edward Azavedo, Dimitra Ntoula
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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. * Equal contribution 2 We define large invasive cancers as those confirmed to have spread and be ≥ 2cm at time of diagnosis. 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks.
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