CSAW-CC (mammography) – a dataset for AI research to improve screening, diagnostics and prognostics of breast cancer
type: Dataset
description: The dataset contains x-ray images, mammography, from breast cancer screening at the Karolinska University Hospital, Stockholm, Sweden, collected by principal investigator Fredrik Strand at Karolinska Institutet. The purpose for compiling the dataset was to perform AI research to improve screening, diagnostics and prognostics of breast cancer. The dataset is based on a selection of cases with and without a breast cancer diagnosis, taken from a more comprehensive source dataset. 1,103 cases of first-time breast cancer for women in the screening age range (40-74 years) during the included time period (November 2008 to December 2015) were included. Of these, a random selection of 873 cases have been included in the published dataset. A random selection of 10,000 healthy controls during the same time period were included. Of these, a random selection of 7,850 cases have been included in the published dataset. For each individual all screening mammograms, also repeated over time, were included; as well as the date of screening and the age. In addition, there are pixel-level annotations of the tumors created by a breast radiologist (small lesions such as micro-calcifications have been annotated as an area). Annotations were also drawn in mammograms prior to diagnosis; if these contain a single pixel it means no cancer was seen but the estimated location of the center of the future cancer was shown by a single pixel annotation. In addition to images, the dataset also contains cancer data created at the Karolinska University Hospital and extracted through the Regional Cancer Center Stockholm-Gotland. This data contains information about the time of diagnosis and cancer characteristics including tumor size, histology and lymph node metastasis. The precision of non-image data was decreased, through categorisation and jittering, to ensure that no single individual can be identified. The following types of files are available: - CSV: The following data is included (if applicable…
alternate name: Cohort of Screen-age Women - Case control (CSAW-CC)
citation: CreativeWork
  name: Dembrower, K., Liu, Y., Azizpour, H., Eklund, M., Smith, K., Lindholm, P., & Strand, F. (2020). Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction. Radiology, 294(2), 265–272. https://doi.org/10.1148/radiol.2019190872
citation: CreativeWork
  name: Dembrower K, Lindholm P, Strand F. A Multi-million Mammography Image Dataset and Population-Based Screening Cohort for the Training and Evaluation of Deep Neural Networks-the Cohort of Screen-Aged Women (CSAW). J Digit Imaging. 2019.
citation: CreativeWork
  name: Dembrower, K., Wahlin, E., Liu, Y., Salim, M., Smith, K., Lindholm, P., Eklund, M., & Strand, F. (2020). Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload : a retrospective simulation study. The Lancet Digital Health, 2(9), E468–E474. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281510
citation: CreativeWork
  name: Salim, M., Wåhlin, E., Dembrower, K., Azavedo, E., Foukakis, T., Liu, Y., Smith, K., Eklund, M., & Strand, F. (2020). External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms. JAMA Oncology, 6(10), 1581. https://doi.org/10.1001/jamaoncol.2020.3321
conditions of access: https://publications.europa.eu/resource/authority/access-right/NON_PUBLIC
conditions of access: info:eu-repo/semantics/restrictedAccess
contributor: SciLifeLab - Royal Institute of Technology
contributor: Regional Cancer Centre Stockholm-Gotland
contributor: Karolinska University Hospital
contributor: Kevin Smith
creative work status: completed
  type: DefinedTerm
  in defined term set: http://publications.europa.eu/resource/authority/dataset-status
  term code: COMPLETED
creator: Fredrik Strand
date modified: 2026-03-20
date published: 2022-04-22
funding: AI in breast cancer screening
  type: Grant
  description: Purpose and goal: To develop and test an AI-system that analyzes mammography images and which can be used to improve the breast cancer screening process. To achieve this, we have built a a database with mammography images in combination with clinical data from the breast cancer registry.
  funder: Vinnova
language: English
is part of: https://doi.org/10.5878/m991-0t11
keywords: Cancer and Oncology
  type: DefinedTerm
  description: Including: Precision Medicine
  term code: 30203
keywords: Radiology and Medical Imaging
  type: DefinedTerm
  term code: 30208
keywords: Breast Neoplasms
keywords: Mammography
license: https://creativecommons.org/licenses/by/4.0/
publisher: Karolinska Institutet
size: QuantitativeValue
  unit text: cases
  value: 8723
size: QuantitativeValue
  unit text: variables
  value: 19
spatial coverage: Stockholm County
temporal coverage: 2008/2015
distribution: documentation/CSAW-CC_breast_cancer_screening_data.csv
  type: DataDownload
  additional type: documentation
  conditions of access: http://publications.europa.eu/resource/authority/access-right/PUBLIC
  conditions of access: info:eu-repo/semantics/openAccess
  content size: 9211529
  encoding format: text/csv
distribution: documentation/CSAW-CC_Readme_annon_230516.docx
  type: DataDownload
  additional type: documentation
  conditions of access: http://publications.europa.eu/resource/authority/access-right/PUBLIC
  conditions of access: info:eu-repo/semantics/openAccess
  content size: 29050
  encoding format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
version: 1

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property ID: DOI
value: 10.5878/45vm-t798

principalRegistrationNumber
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property ID: https://www.rikstermbanken.se/termposter/1848/diarienummer
value: 4-3790/2016

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value: 10.1148/radiol.2019190872

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value: urn:nbn:se:kth:diva-267834

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value: 10.1007/s10278-019-00278-0

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value: 10.1016/S2589-7500(20)30185-0

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SciLifeLab - Royal Institute of Technology
type: Organization
name: Royal Institute of Technology
department: SciLifeLab

Karolinska University Hospital
type: Organization
department: Breast Radiology

Kevin Smith
type: Person
affiliation: SciLifeLab - Royal Institute of Technology
family name: Smith
given name: Kevin

Fredrik Strand
type: Person
affiliation: Karolinska Institutet
family name: Strand
given name: Fredrik

Karolinska Institutet
type: Organization
department: Department of Oncology-Pathology

Vinnova
type: Organization

English
type: Language
alternate name: eng

Breast Neoplasms
type: DefinedTerm
description: Tumors or cancer of the human BREAST.
term code: D001943

Mammography
type: DefinedTerm
description: Radiographic examination of the breast.
term code: D008327

Stockholm County
type: Place