Brain Tumor Radiogenomic Classification

This is the capstone project for the Machine Learning Nanodegree through Udacity. This project is based on the Kaggle competition hosted by the Radiological Society of North America (RSNA). The goal of the this project is to build a model to detect the presence of MGMT promoter methylation in a sequence of magnetic resonance imaging (MRI) sequences. MRI sequences of the brain are taken along three planes: coronal, axial, and sagittal (Figure 1). A typical set MRI sequences will have approximately 400 images. In addition, this project demonstrates the full pipeline of data preparation, feature extraction, and model training on Amazon Web Services (AWS).

AWS Requirements

This project was built using the following AWS² services:

1. Sagemaker Notebook Instance - ml.m4.xlarge
2. S3 Storage - s3://Sagemaker_Notebook_Bucket
3. Training Instance - ml.p2.2xlarge
4. IAM - secrete access key

Model Requirements

All necessary prep and feature engineering steps are included in the notebook 01_data_exploration.ipynb. This project uses the 3D-CNN model EfficientNet-3D³ with some modifications to account for binary classification. Training is done on the GPU instance ml.p2.2xlarge.

Code Structure

All source code is located in the main project directory under ./source. The following directories are included in the project:

1. ./source/helpers.py - Helper functions for the project to process images and format dataset for training.
2. ./source/train.py - Training code for the project.
3. ./source/predict.py - Prediction code for the project.
4. ./source/requirements.txt - Requirements for loading external libraries on the training instance.

Data

Data¹ for the project is hosted on S3. It is available for download from from the Kaggle competition page and is approximatly 127 GB with the following structure:

Running the Notebook

In order to run the notebook and train, all resources must be available on the training instance with access to the S3 bucket that stores the data.

References

[1] https://www.kaggle.com/c/rsna-miccai-brain-tumor-radiogenomic-classification/data

[2] https://aws.amazon.com/sagemaker/

[3] https://github.com/shijianjian/EfficientNet-PyTorch-3D/blob/master/example.py