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CDD-CESM-Dataset

This is a helper repository for the CDD-CESM Mammogram Dataset containing all the tools for pre-processing and segmentation model

Dataset Link: here

Paper Link: here

Thesis link here.

Installation & Usage

The project was tested on a virtual environment of python 3.7, pip 23.2.1, and MacOS

  • pip install -r full_requirements.txt (or pip install -r requirements.txt if there are errors because of using a different operating system, as requirements.txt only contains the main dependencies and pip will fetch the compatible sub-dependencies, but it will be slower)
  • Download dataset
  • Put the images inside dataset/images
  • split annotations into dataset/train_set.csv and dataset/test_set.csv
  • edit configs.py to configure the training process
  • run train.py to train a classification model
  • run test.py to test a classification model
  • run parse_reports.py to parse the full reports and convert them to csv
  • run clean_images_names.py to remove any spaces from the images' names
  • run parse_reports.py to parse the full reports and convert them to csv
  • run draw_activations.py to draw gradcam activations from a trained model
  • run evaluate_segmentation_model.py to evaluate the segmentations from a trained classification model using the method in the paper and save the images
  • run draw_real_segmentations.py to draw the segmentations from the segmentation annotations

Automatic Segmentation Flow & Example Results

Citation

If you use this dataset, please cite the following:

  • Khaled R., Helal M., Alfarghaly O., Mokhtar O., Elkorany A., El Kassas H., Fahmy A. Categorized Digital Database for Low energy and Subtracted Contrast Enhanced Spectral Mammography images [Dataset]. (2021) The Cancer Imaging Archive. DOI: 10.7937/29kw-ae92

  • Khaled, R., Helal, M., Alfarghaly, O., Mokhtar, O., Elkorany, A., El Kassas, H., & Fahmy, A. Categorized contrast enhanced mammography dataset for diagnostic and artificial intelligence research. (2022) Scientific Data, Volume 9, Issue 1. DOI: 10.1038/s41597-022-01238-0

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