Augmented Chest X-Ray

Introduction

Augmented Chest X-Ray is a tool that uses data augmentation for training CNN models specialized in multi-label classification of thorax anomalies in X-ray images.

• Optimized for training on the NIH Chest X-ray Dataset, introduced by Wang et al.¹
• Uses a 121-layer Dense Convolutional Neural Network (DenseNet), pretrained on ImageNet, with an added sigmoid nonlinearity, as discussed by Rajpurkar et al. in CheXNet.²
• Produces a trained model along with classification thresholds for each of 14 pathologies.
• Implemented in PyTorch.
• Enhanced with data augmentation from Albumentations.

Table of Contents

1. Using the Repository
   • Requirements
   • Installation
   • The Dataset
   • Preprocessing
   • Training
   • Validation on the Test Set
2. Comparison of Minimal vs. Advanced Data Augmentation
   • Comparison Setup
   • Implementation
   • Results
   • Parameters for Reproducing the Comparison
3. Reference

Using the Repository

Requirements

• Python 3.8 or higher

Ensure you have the correct version of Python installed before starting the installation.

Installation

1. Clone the project repository

Clone the project repository from GitHub using either of following methods:

• HTTPS:

git clone https://github.com/MichaelNoya/augmented-chest-xray.git

• SSH:

git clone git@github.com:MichaelNoya/augmented-chest-xray.git

2. Change into the cloned directory and create the venv environment

cd augmented-chest-xray
python -m venv env

3. Activate the venv environment

• Windows:

env\Scripts\activate

• Linux:

source env/bin/activate

4. Update pip

Ensure you have the latest version of pip by updating it with the following command:

python -m pip install --upgrade pip

5. Install PyTorch

To install PyTorch, please visit the Get Started page on the official PyTorch website. Select your OS and CUDA version for your hardware, and run the provided pip command.

6. Install the Other Required Packages

Install all the necessary dependencies by executing the following command in your virtual environment:

pip install -r requirements.txt

The Dataset

To train the classifier you require the NIH Chest X-ray Dataset. You can either download the full dataset (45GB), or a sample subset (1GB). The sample subset is already preprocessed and ready-to-use.

Download the Full Dataset

The full NIH Chest X-ray Dataset contains 112,120 X-ray images from 30,805 unique patients. It can be downloaded from the National Institutes of Health - Clinical Center at this link. The required files are:

• The extracted images
• Data_Entry_2017_v2020.csv, which contains the class labels and patient data.

Save the extracted images in ./augmented_chest_xray/data/original_data/images/ and the data entry file in ./augmented_chest_xray/data/original_data/meta_data/ and proceed to Preprocessing.

Download the Sample Subset

The sample subset consists of 30,545 X-ray images of 8,400 unique patients. It can be found in the nih-chest-xray-webdataset-subset repository. Copy all the files from the ./datasets/ and ./labels/ directories into the ./augmented_chest_xray/data/datasets/ and ./augmented_chest_xray/data/labels/ directories. Since this subset is already preprocessed, you can skip the preprocessing step and go directly to Training.

Preprocessing

Use preprocess_data.py to perform the following steps:

• Split the data into training, validation and test sets with no patient overlap between the sets.
• Downscale the images and pack them into WebDataset tar archives for sequential data access.

Run the preprocessing with default parameters:

python preprocess_data.py

Optional Parameters

• -r or --random_seed: Seed for the random split of the dataset.
• -s or --subset_size: If a subset_size is specified, the training, validation and test sets will be created using images of only a subset of the patients from the original dataset.

Default Values

The following values can be adjusted in config.py:

# Size in pixel for downscaling the images
DATASET_IMAGE_SIZE = 256

# Size ratio of the datasets
TRAIN_VAL_TEST_RATIO = (0.7, 0.1, 0.2)

# Input filenames and directories
IMAGE_DIR = './data/original_data/images/'
META_DATA_DIR = './data/original_data/meta_data/'
META_FILENAME = 'Data_Entry_2017_v2020.csv'

# Output filenames and directories
LABELS_DIR = './data/labels/'
DATASET_DIR = './data/datasets/'
TRAIN_LABELS = 'train_labels.csv'
VAL_LABELS = 'val_labels.csv'
TEST_LABELS = 'test_labels.csv'

Training

Use train_model.py to train a CNN image classifier. After each epoch of training the model is validated on the validation dataset. If the validation loss is lower than the loss in previous epochs, the state_dict containing the weights of the model is stored in a dictionary along with the corresponding thresholds for each class. This dictionary is saved as <year_date_time>_model.pt and overwritten, whenever a lower validation loss is measured during the same training session. Additionally, training results are logged and saved as <year_date_time>_results.pt after each epoch. The results include the mean AUROC and mean F1 score as well as the training and validation loss for each epoch.

To train a new model using default parameters type:

python train_model.py

Optional Parameters

• -b or --batch_size: Specify a mini batch size to use during training and validation.
• -w or --workers: Turn on multi-process data loading with the specified number of loader worker processes.
• -l or --learning_rate: Set a learning rate for the optimizer initialization.
• -f or --s_factor: Factor by which the scheduler should reduce the learning rate.
• -p or --s_patience: Number of epochs with no improvement that the scheduler should wait before decreasing the learning rate.
• -a or --augmentation: Activate data augmentation.
• -d or --debug: Reduce the size of the datasets for debugging.
• -s or --saved_model: File to load a state_dict from a previous training session.

Examples

Train a model for twelve epochs using the default values described in the CheXNet paper:

python train_model.py -b 16 -e 12 -l 0.001 -f 0.1

This example loads a model saved in my_model.pt and fine-tunes it for seven epochs using data augmentation:

python train_model.py -b 16 -e 7 -l 0.001 -s my_model.pt -a

Validation on the Test Set

Use validate_model.py to validate a trained model on the test set. The output is a table containing the area under the ROC (AUROC) for each class as well as the mean AUROC over all classes.

Validate the latest saved model:

python validate_model.py

If the model was trained using data augmentation, the validation performance can be improved by using the center crop flag:

python validate_model.py --center_crop

Optional Parameters

• -s or --saved_model: Name of file from which to load the state_dict of a trained model. If no filename is provided, the most recently created *_model.pt file will be loaded.
• -b or --batch_size: Specify a mini batch size to use during validation.
• -w or --workers: Turn on multi-process data loading with the specified number of loader worker processes.
• -c or --center_crop: Center crop the images of the test set. Use this flag if the dataset was enhanced with data augmentation during training.
• -d or --debug: Reduce the size of the dataset for debugging.
• -f or --display_f1: Display the F1 Scores for each class and the mean F1 Score in the results table.

Comparison of Minimal vs. Advanced Data Augmentation

Comparison Setup

We compared the validation loss and test results of a group of models trained using only minimal data augmentation to a group of models trained using a combination of various forms of data augmentation. While one group used only horizontal flipping, the other group additionally used a combination of random shifts, rotations and image cropping, as well as random changes in scaling, perspective, brightness and contrast.

Out of each group five models were trained for eight epochs each. For easier comparison, the learning rate during training was was fixed at 0.0001 for all training runs, with no learning rate decay. The models with the lowest validation loss in their respective group were selected for validation on a separate test set. The error metric used for the final evaluation was the mean area under the ROC (AUROC) over all classes.

Implementation

The data augmentation pipelines for the training dataset are implemented in the module transforms/augmented_transforms.py using image transforms imported from Albumentations. Default data augmentation parameters can be adjusted inside the module under the docstring block titled Data augmentation parameters.

Results

When comparing the training and validation loss measured after each epoch we can observe a clear regularizing effect caused by the data augmentation. This also affects the number of epochs the model needs to be trained for the validation loss to reach a minimum. The group with the minimal data augmentation reached its lowest validation loss after two to three epochs while the group with advanced data augmentation reached its lowest validation loss after five epochs on average.

Training and validation loss using minimal data augmentation (left) and advanced data augmentation (right).

As expected, lower validation loss resulted to be a predictor for superior performance on the test set. In the group with minimal data augmentation, the lowest measured validation loss was 0.1495. The corresponding model had a mean AUROC of 0.8153 on the test set. In the group trained using advanced data augmentation the lowest validation loss was 0.1466 and the corresponding model achieved a mean AUROC of 0.8308.

Parameters for Reproducing the Comparison

Train the model with minimal data augmentation and validate it on the test set:

%run train_model.py -b 16 -e 8 -l 0.0001 -p 8
%run validate_model.py -b 32

Train the model with advanced data augmentation and validate it on the test set:

%run train_model.py -b 16 -e 8 -l 0.0001 -p 8 -a
%run validate_model.py -b 32 -c

Reference

1. ^ Wang et al. ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases, 2017

2. ^ Rajpurkar et al. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning, 2017