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Digital mammography dataset for breast cancer diagnosis research (DMID) with breast mass segmentation analysis

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Abstract

Purpose:In the last two decades, computer-aided detection and diagnosis (CAD) systems have been created to help radiologists discover and diagnose lesions observed on breast imaging tests. These systems can serve as a second opinion tool for the radiologist. However, developing algorithms for identifying and diagnosing breast lesions relies heavily on mammographic datasets. Many existing databases do not consider all the needs necessary for research and study, such as mammographic masks, radiology reports, breast composition, etc. This paper aims to introduce and describe a new mammographic database. Methods:The proposed dataset comprises mammograms with several lesions, such as masses, calcifications, architectural distortions, and asymmetries. In addition, a radiologist report is provided, describing the details of the breast, such as breast density, description of abnormality present, condition of the skin, nipple and pectoral muscles, etc., for each mammogram. Results:We present results of commonly used segmentation framework trained on our proposed dataset. We used information regarding the class of abnormalities (benign or malignant) and breast tissue density provided with each mammogram to analyze the segmentation model’s performance concerning these parameters. Conclusion:The presented dataset provides diverse mammogram images to develop and train models for breast cancer diagnosis applications.

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Data availability

The dataset can be used to train ML or DL models for mammogram classification, BI-RADS classification, as well as breast composition classification. Moreover, it can be used to train segmentation models to segment breast lesions. Additionally, the radiology reports can be utilized to train report generation models. This dataset will be available for research purposes only on the link provided in the article.

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Acknowledgements

We would like to thank Samved Hospital, Ahmedabad, India. Additionally, we would like to thank Dr. Dinesh Patel and Dr. Trupti Patel for their support.

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Authors and Affiliations

  1. Nirma University, Ahmedabad, India

    Parita Oza & Pankaj Kumar

  2. Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India

    Urvi Oza & Bakul Gohel

  3. Rad Imaging, X-Ray and Sonography Clinic, Ahmedabad, India

    Rajiv Oza

  4. Pandit Deendayal Energy University, Gandhinagar, India

    Paawan Sharma & Samir Patel

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  1. Parita Oza
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Correspondence to Parita Oza.

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The use of the dataset for the purpose of AI research has been approved by the Ethical Review Board.

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Oza, P., Oza, U., Oza, R. et al. Digital mammography dataset for breast cancer diagnosis research (DMID) with breast mass segmentation analysis. Biomed. Eng. Lett. 14, 317–330 (2024). https://doi.org/10.1007/s13534-023-00339-y

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