BREAST MASSES DATASET WITH PRECISELY ANNOTATED SEQUENTIAL MAMMOGRAMS Citing the Dataset The dataset is released under a Creative Commons Attribution license, so it is mandatory to cite the dataset if you use it in your work in any form. Published academic papers should use the academic paper citation of our paper. Personal works, such as projects or blog posts, should provide a URL to this Zenodo page, though a reference to our paper would also be appreciated. Academic paper citation Loizidou, K., Skouroumouni, G., Savvidou, G. et al. Subtraction of Temporally Sequential Digital Mammograms: Enhancing the Detection and Classification of Malignant Masses in Breast Imaging. IEEE Open Journal of Engineering in Medicine and Biology (2025). https://doi.org/10.1186/s41747-021-00238-w Personal use citation Include a link to this Zenodo page - 10.5281/zenodo.11446259 ACKNOWLEDGMENT The publication of this paper is supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 739551 (KIOS CoE) and the Government of the Republic of Cyprus through the Cyprus Deputy Ministry of Research, Innovation and Digital Policy. Contact Information If you would like further information about the dataset, or if you experience any issues downloading files, please contact us at cloizi01@ucy.ac.cy. General Information This dataset consists of 100 pairs of mammograms, from two temporally sequential rounds. Specifically, this dataset includes the prior and recent mammograms with two mammographic views for each patient. This is a complete dataset for the detection and classification of breast masses, using sequential mammograms. It contains normal (BI-RADS 1), benign (BI-RADS 2), and biopsy-confirmed malignant cases (BI-RADS 6). For each mammogram, an image with precise annotation of each individual mass, by two expert radiologists, is provided. More details are available in the README.txt

Another dataset related only to breast micro-calcifications can be accessed here: 10.5281/zenodo.14859694 Breast Micro-Calcifications Dataset with Precisely Annotated Sequential Mammograms