CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2021
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914198391357440 |
|---|---|
| author | Sorkhei, Moein Liu, Yue Azizpour, Hossein Azavedo, Edward Dembrower, Karin Ntoula, Dimitra Zouzos, Athanasios Strand, Fredrik Smith, Kevin |
| author_facet | Sorkhei, Moein Liu, Yue Azizpour, Hossein Azavedo, Edward Dembrower, Karin Ntoula, Dimitra Zouzos, Athanasios Strand, Fredrik Smith, Kevin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2112_01330 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer Sorkhei, Moein Liu, Yue Azizpour, Hossein Azavedo, Edward Dembrower, Karin Ntoula, Dimitra Zouzos, Athanasios Strand, Fredrik Smith, Kevin Computer Vision and Pattern Recognition Machine Learning 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. |
| title | CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2112.01330 |