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 and the GitHub repo here.CSAW-M Research Use LicensePlease 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} }欢迎来到CSAW-M数据集主页。本页面收录与CSAW-M相关的全部文件与元数据。CSAW-M是一份经精选的乳腺X光影像（mammogram）数据集，附带针对癌症遮蔽效应（masking of cancer）的专家评估结果。该数据集采集自逾1万名受试者，并针对潜在遮蔽效应完成标注。与此前以乳腺影像密度作为替代指标的研究范式不同，本数据集直接提供了五位专家对遮蔽潜力的评估标注。我们基于CSAW-M训练深度学习模型以估计遮蔽程度，实验表明，在未针对该任务进行显式训练的前提下，模型预测的遮蔽程度相较乳腺密度指标，能更显著地预判筛查受试者中被诊断为间隔期癌症与大体积浸润性癌的情况。本研究的相关论文与GitHub代码仓库链接分别见对应位置。 CSAW-M数据集研究使用许可 请仔细阅读CSAW-M数据集研究使用许可的全部条款与细则。 数据集获取方式：若您希望获取本数据集，请使用页面上方的“申请文件访问权限”选项（目前非瑞典籍研究人员需拥有通用figshare账号，方可提交访问申请）。我们将要求您同意本许可条款，并提供您使用该数据集的具体用途说明。我们会在收到申请后完成审核，审核通过后您即可下载全部数据集文件。 若您使用本数据集，请引用我们的论文： @article{sorkhei2021csaw, title={CSAW-M：面向癌症乳腺影像遮蔽效应基准测试的序数分类数据集}, author={Sorkhei, Moein and Liu, Yue 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} }