Name
Title | ||
|---|---|---|
CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer. |
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2021 | Annual Conference on Neural Information Processing Systems | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Moein Sorkhei | 1 | 0 | 0.34 |
| Yue Liu | 2 | 0 | 0.34 |
| Hossein Azizpour | 3 | 1376 | 61.66 |
| Edward Azavedo | 4 | 5 | 1.18 |
| Karin Dembrower | 5 | 1 | 1.02 |
| Dimitra Ntoula | 6 | 0 | 0.34 |
| Athanasios Zouzos | 7 | 0 | 0.68 |
| Fredrik Strand | 8 | 0 | 0.68 |
| Kevin Smith | 9 | 2430 | 88.78 |