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

dataset
posted on 19.11.2021, 15:27 by Moein SorkheiMoein Sorkhei, Yue LiuYue Liu, Hossein Azizpour, Edward Azavedo, Karin Dembrower, Dimitra Ntoula, Anthanasios Zouzos, Fredrik Strand, Kevin SmithKevin Smith
Welcome to the the CSAW-M dataset homepage

This page includes the files and metadata related to the CSAW-M, a curated dataset of mammograms with expert assessments of the masking of cancer.

CSAW-M is 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 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.

Please find the paper corresponding to our work here and the GitHub repo here.


CSAW-M Research Use License

Please read carefully all the terms and conditions of the CSAW-M Research Use License.


How to access the dataset:

If you want to get access to the data, please use the "Request access to files" option above (currently, non-Swedish researchers need to have a general figshare account to be able to to request access). We will ask you to agree to our terms of conditions and provide us with some information about what you will use the data for. We will then receive the request and process it, after which you would be able to download all the files.


If you use this Work, please cite our paper:

@article{sorkhei2021csaw,
  title={CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer},
  author={Sorkhei, Moein and Liu, Yue and Azizpour, Hossein and Azavedo, Edward and Dembrower, Karin and Ntoula, Dimitra and Zouzos, Athanasios and Strand, Fredrik and Smith, Kevin},
  year={2021}
}

History

Publisher

KTH Royal Institute of Technology

Access request email

sorkhei@kth.se

Contact email

sorkhei@kth.se