lorepas / CNN-medical-imaging-analysis

This project has the aim to detect abnormality in mammography, using a CNN trained from scratch, a pretrained CNN and an Ensamble methodology to notice any improvements

Convolutional Neural Network for Medical Imaging Analysis - Abnormality detection in mammography

This project has the aim to detect abnormality in mammography.

Dataset

The dataset we will focus on is CBIS DDSM: Curated Breast Imaging Subset of Digital Database for Screening Mammography. In the images are present two types of abnormality: mass or calcification. Then we can distinguish between benign or malignant. For this reason we have the following classes:

• Mass, Benign (with or without callback)
• Mass, Malignant
• Calcification, Benign
• Calcification, Malignant

Original images have a high resolution, e.g. 3000x4000 and they are grayscale images with a depth of 16bit. We use a numpy arrays containing images and labels from training and test sets. The steps performed on each original image are described below:

• the abnormality patch has been extracted from the original image according to the binary mask;
• a patch of healthy tissue (baseline patch) adjacent to the abnormality patch has been extracted from the original image (left, right, top or bottom - no overlap). Both abnormality patch and baseline patch have been added to the images tensor; in other words, an abnormality patch has been ignored if a related baseline patch could not be extracted.
• both abnormality patch and baseline patch have been resized to shape (150x150) using OpenCV resize function: cv2.resize(img, dsize=(shape, shape), interpolation=cv2.INTER_NEAREST)
• class labels have been assigned to the patches according to the following mapping:
  • 0: Baseline patch
  • 1: Mass, benign
  • 2: Mass, malignant
  • 3: Calcification, benign
  • 4: Calcification, malignant
• images of baseline patch and abnormality patch, and their related labels, have been added to distinct numpy arrays for images and labels.
  • train_tensor.npy: images tensor for training
  • train_labels.npy: labels tensor for training
  • public_test_tensor.npy: images tensor for test
  • public_test_labels.npy: images tensor for test

In light of the procedure described above, particular attention should be paid to the structure of the input tensors:

• odd indices [2i + 1 for i in range(0,len(tensor)/2)] will refer to abnormality patches
• previous even indices [2i for i in range(0,len(tensor)/2)] will refer to respective baseline patches You will be able to load the arrays using numpy load function.

Project structure

The project is structured with the following files in .ipynb format because performed on Colab notebooks:

1. TASK 1: Perform the classification building a CNN from scratch:
   • Scratch_CNN_2.1.ipynb: in this notebook we perform the classification between mass and calcification.
   • Scratch_CNN_2.2.ipynb: in this notebook we perform the classification between benign and mailignant.
2. TASK 2: Perform the classification using a pre-trained network:
   • PreTrained_CNN_3.1.ipynb: in this notebook we perform the classification between mass and calcification.
   • PreTrained_CNN_3.2.ipynb: in this notebook we perform the classification between benign and mailignant.
3. TASK 3: Perform the classification to distinguish the abnormality considering also the baseline patches:
   • BaselineCNN.ipynb: in this notebook we perform the classification between mass and calcification but considering also the baseline patches. For this reason we have used a Siamese Network.
4. TASK 4: Perform the classification task using an Ensamble method:
   • Ensamble1.ipynb: in this notebook we perform the classification between mass and calcification using an Ensamble method.
   • Ensamble2.ipynb: in this notebook we perform the classification between benign and malignant using an Ensamble method.