Literature DB >> 24784381

Provision of the DDSM mammography metadata in an accessible format.

Matthias Benndorf1, Christoph Herda2, Mathias Langer1, Elmar Kotter1.   

Abstract

PURPOSE: The Digital Database for Screening Mammography (DDSM) is the largest publicly available resource for mammographic image analysis research and has been used extensively in the past for computer assisted diagnosis (CADx) studies. However, the database has not been searchable for a specific kind of lesion, which rendered the case selection process in past studies often times arbitrary. Therefore, the authors want to provide the complete metadata of the DDSM in an accessible format.
METHODS: The authors semiautomatically transformed the data available athttp://marathon.csee.usf.edu/Mammography/Database.html into table format. The 1769 cases (914 from cancer volumes, 855 from benign volumes) comprise 1220 mass lesions (578 benign, 642 malignant) and 859 calcifications (433 benign, 426 malignant). Additionally, 694 normal cases were processed to allow for matching according to age and breast density.
RESULTS: The authors provide the entire DDSM metadata (for benign, malignant, and normal cases) as tab-delimited text files[see supplementary material at http://dx.doi.org/10.1118/1.4870379E-MPHYA6-41-006405 for DDSM metadata].
CONCLUSIONS: The data provided make the case selection for future studies using the DDSM reproducible. Furthermore, it may serve as a validation dataset for CADx approaches using the BI-RADS lexicon.

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Mesh:

Year:  2014        PMID: 24784381     DOI: 10.1118/1.4870379

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  4 in total

1.  External validation of a publicly available computer assisted diagnostic tool for mammographic mass lesions with two high prevalence research datasets.

Authors:  Matthias Benndorf; Elizabeth S Burnside; Christoph Herda; Mathias Langer; Elmar Kotter
Journal:  Med Phys       Date:  2015-08       Impact factor: 4.071

2.  Evaluation of Feature Selection Methods for Mammographic Breast Cancer Diagnosis in a Unified Framework.

Authors:  Chun-Jiang Tian; Jian Lv; Xiang-Feng Xu
Journal:  Biomed Res Int       Date:  2021-10-04       Impact factor: 3.411

Review 3.  [Development of an Optimized Deep Learning Model for Medical Imaging].

Authors:  Young Jae Kim; Kwang Gi Kim
Journal:  Taehan Yongsang Uihakhoe Chi       Date:  2020-11-30

4.  Deep learning can be used to train naïve, nonprofessional observers to detect diagnostic visual patterns of certain cancers in mammograms: a proof-of-principle study.

Authors:  Jay Hegdé
Journal:  J Med Imaging (Bellingham)       Date:  2020-02-04
  4 in total

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