Child pages
  • The Chinese Mammography Database (CMMD)
Skip to end of metadata
Go to start of metadata

Summary

Breast carcinoma is the second largest cancer in the world among women. Early detection of breast cancer has been shown to increase the survival rate, thereby significantly increasing patients' lifespans. Mammography, a noninvasive imaging tool with low cost, is widely used to diagnose breast disease at an early stage due to its high sensitivity. The recent popularization of artificial intelligence in computer-aided diagnosis creates opportunities for advances in areas such as (1) Computer-aided detection for locating suspect lesions such as mass and microcalcification, leaving the classification to the radiologist; and (2) Computer-aided diagnosis for characterizing the suspicious region of lesion and/or estimate its probability of onset; and (3) Findings of predictive image-based biomarkers by applying the computational methods to mine the potential relationships between image representation and molecular subtype, including luminal A, luminal B, HER2 positive, and Triple-negative.

However, existing publicly available mammography databases are limited by small sample size, lack of diversity in patient populations, missing biopsy confirmations and unknown molecular sub-types.  To help fill the gap, we built a database conducted on 1,775 patients from China with benign or malignant breast disease who underwent mammography examination between July 2012 and January 2016. The database consists of 3,728 mammographies from these 1,775 patients, with biopsy confirmed type of benign or malignant tumors. For 749 of these patients (1,498 mammographies) we also include patients' molecular subtypes. Image data were acquired on a GE Senographe DS mammography system.  


Acknowledgements

  • The authors of this dataset thank the volunteers from the School of Computer Science and Engineering, South China University of Technology for assisting to tidy the clinical and imaging data. This work was supported by the grant from the National Natural Science Foundation of China (no.61771007).
  • This work was partially supported by the Key-Area Research and Development of Guangdong Province under Grant (2020B010166002, 2020B1111190001), the National Natural Science Foundation of China (61472145, 61771007), Guangdong Natural Science Foundation (2017A030312008), and the Health & Medical Collaborative Innovation Project of Guangzhou City (201803010021, 202002020049).
  • Harmonization of the components of this dataset, including into standard DICOM representation, was supported in part by the NCI Imaging Data Commons consortium. NCI Imaging Data Commons consortium is supported by the contract number 19X037Q from Leidos Biomedical Research under Task Order HHSN26100071 from NCI.

Data Access

Data TypeDownload all or Query/Filter

Images (DICOM, 22.9 GB)

   

(Download requires the NBIA Data Retriever)

Clinical data (XLSX)

Click the Versions tab for more info about data releases.

Please contact help@cancerimagingarchive.net  with any questions regarding usage.

Detailed Description

Image Statistics


Modalities

MG

Number of Patients

1775

Number of Studies

1775

Number of Series

1775

Number of Images

5202

Images Size (GB)22.9 GB
  • Mammography images were collected in .TIFF format and converted to DICOM.
  • Clinical data are saved in .XLSX format. Note that for those rows where there exists BOTH a value for ID1 and ID2, TCIA image database stores ONLY the ID2 value as PatientID.


Citations & Data Usage Policy

Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution 4.0 International License under which it has been published. Attribution should include references to the following citations:


Data Citation

Cui, Chunyan; Li Li; Cai, Hongmin; Fan, Zhihao; Zhang, Ling; Dan, Tingting; Li, Jiao; Wang, Jinghua. (2021) The Chinese Mammography Database (CMMD): An online mammography database with biopsy confirmed types for machine diagnosis of breast. The Cancer Imaging Archive. DOI: https://doi.org/10.7937/tcia.eqde-4b16

Publication Citation

  1. Cai, H., Huang, Q., Rong, W., Song, Y., Li, J., Wang, J., Chen, J., & Li, L. (2019). Breast Microcalcification Diagnosis Using Deep Convolutional Neural Network from Digital Mammograms. Computational and Mathematical Methods in Medicine, 2019, 1–10. https://doi.org/10.1155/2019/2717454

  2. Wang, J., Yang, X., Cai, H., Tan, W., Jin, C., & Li, L. (2016). Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning. Scientific Reports, 6(1). https://doi.org/10.1038/srep27327

TCIA Citation

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7

Other Publications Using This Data

TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.

Version 1 (Current): Updated 2021/04/06

Data TypeDownload all or Query/Filter

Images (DICOM, 22.9 GB)

   

(Download requires the NBIA Data Retriever)

Clinical data (XLSX)





  • No labels