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

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 (https://arxiv.org/abs/2112.01330) and the GitHub repo here (https://github.com/yueliukth/CSAW-M) . CSAW-M Research Use License Please read carefully all the terms and conditions of the CSAW-M Research Use License (https://drive.google.com/file/d/1AwPjQnzfEIOiXlDSvkmLLhK5YEKiNaIG/view?usp=sharing) . 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 (https://figshare.com/account/register) 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}, ᵧₑₐᵣ={₂₀₂₁} }