CBIS-DDSM-DATASET

Suppose you want to train a breast cancer classifier or a segmentation model using the CBIS-DDSM dataset. In that case, this repository may help you to easily extract the mammograms and the masks from the original folder.

🔧 Setup Instructions

1. Download the Dataset

You can download the dataset directly from the official TCIA page.

a. Install the NBIA Data Retriever

To download the dataset, you’ll need the NBIA Data Retriever, a tool provided by TCIA:

• Click here to download NBIA Data Retriever

b. Ubuntu Linux Installation Guide

As an Ubuntu Linux user, here’s a step-by-step guide to installing the NBIA Data Retriever and then the dataset:

1. Download the .deb installer to your computer.

2. Open a terminal, navigate to the directory that contains the .deb installer, and run the following command:

   sudo dpkg -i nbia-data-retriever_4.4.3-1_amd64.deb
   

   💡 Take into account that you should replace nbia-data-retriever_4.4.3-1_amd64.deb by the actual name of the installer you downloaded before.

3. Download the .tcia file that contains the mammograms to your computer.

4. Open the terminal and navigate to the directory that contains the .tcia file, and run the following command:

5. /opt/nbia-data-retriever/bin/nbia-data-retriever --cli CBIS-DDSM-All-doiJNLP-zzWs5zfZ.tcia
   

   📁 This command will begin downloading the dataset into folders containing the mammogram images and masks. If your .tcia file has a different name, update the command accordingly.

Additional information

To go into detail about the CBIS-DDSM dataset, you can check this paper. It describes how to use the dataset and how the dataset was built.

📊 Quantitative Description

Despite the paper stating that CBIDS-DDSM has 753 calcification cases and 891 mass cases, it is difficult to determine how many images this dataset has. According to the metadata provided in the CSV files, CBIS-DDSM contains 3103 mammograms, 465 of which have more than one abnormality. 2.458 (79.21%) of the mammograms belong to the training set, and 645 (20.79% ) belong to the test set. Furthermore, the dataset includes 3,568 cropped mammograms along with their corresponding masks, which provide pixel-level annotations of abnormalities as identified by a radiologist.

Repository Function Overview

main_retrieval.py

This script generates two CSV files: training_dataset.csv and test_dataset.csv. The data split follows the official partition provided by the dataset's authors.

Each CSV file contains three columns:

1. Mammogram Path: The local file path (location your machine) to each mammogram image.
2. Mask Path: The local file path of each annotation mask. Each mask is matched to its respective mammogram.
3. Label : The classification label for each mammogram, defined as:
   • 1 – Malignant Calcification
   • 2 – Benign Calcification
   • 3 – Malignant Mass
   • 4 – Benign Mass

retrieval_utils.py

This script contains three functions:

• mam_data: This function retrieves the path of all mammograms (3103) in your local machine.
• mask_data: This function retrieves the path of all masks (3568) in your local machine.
• final_dataset: This function merges the mammograms and masks dataframes based on the image id. It outputs two CSV files: one for the training set and one for the test set.

convert_dicom.ipynb:

The images provided by CBIS-DDSM (mammograms, masks, and crops of abnormalities) are saved in DICOM format. This function saves a 16-bit mammogram from DICOM as a rescaled 16-bit PNG file.

🎁 Bonus

In this repository, I implemented the deep learning classifier introduced in the paper "Deep Learning to Improve Breast Cancer Detection on Screening Mammography" using PyTorch and CBIS-DDSM dataset. The original code and model are available here. However, this code is in Keras.
My PyTorch version aims to be more readable and beginner-friendly, especially for researchers and students working on medical deep learning applications.