Data Augmentation and Preprocessing for Limited Datasets
By: Neelabh Pareek
Tags: Facial Recognition, Data Augmentation, Paper Review, Data Preprocessing, Covid 19

Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets

The first paper is from the medical industry.

In recent events, the COVID-19 global pandemic has caused the deaths of many people across the world. The research team (Oh et al.) were motivated to use deep learning to try to successfully provide early diagnosis of COVID-19 and a few other diseases using patient chest x-rays.

Architecture

The research team believed that the necessary information to diagnose COVID-19 could be found in the lungs. The input dataset is a chest x-ray image and therefore contains much more information than just the lungs. Oh et al. used a two stage architecture.

The first stage is a model to pull out only the lung area from the entire chest x-ray image. The team obtained chest x-ray data and preprocessed it to normalize it. This normalization removed any negative impacts from bit depth, compression type, image size, scanning protocol, etc. Specifically, the images were preprocessed by: typecasting from uint8/uint16 to float32, histogram equalized for gray levels to be between 0 and 255, gamma correction applied to 0.5, and all images resized to 256x256.

The normalized dataset was input into a fully connected DenseNet103 (FC-DenseNet103) segmentation network. The result is a mask that can be applied to an image to obtain only the lung contour from the chest x-ray. The research team used an FC-DenseNet103 network to classify between 4 categories: background, heart, left lung, and right lung.

Key details about their segmentation network:

FC-DenseNet103

Cross entropy loss

Weights initialized with random distribution

Adam optimizer

Initial learning rate of 0.0001 with a decay set to 10x on the basis of training loss performance

Early stopping implemented on the basis of performance against validation dataset

Batch size of 2

PyTorch based software suite

The second stage aims to classify the lung contours into 5 different classes: normal, bacterial pneumonia, tuberculosis (TB), viral pneumonia, and COVID-19. Before training could begin for the second stage, the research team performed some data augmentation steps.

The lung contour size was 1024 x 1024. The research team took this data and randomly created smaller images of 224 x 224. From each large image, they created 100 smaller images, called patches in the paper. To ensure that the random patch was not generated from an area where no lung pixel data existed, the center of the patch was chosen from within the lung contour. This artificially created 100 times the data the research team had.

For training this network, the input was the set of images sized at 224 x 224. The network initialized the weights with a pre-trained ImageNet model. Using a pre-trained model should significantly reduce their training time. This is especially helpful with their limited dataset.

Key details about their classification network:

ImageNet

Pre-trained weights for initialization

Adam optimizer

Learning rate of 0.00001

Early stopping implemented on the basis of performance against validation dataset

Batch size of 16

Weight decay and L₁ regularization for overfit prevention

PyTorch based software suite

The trained model results in classification of a patch into 1 of the 4 classes. The research team used a weighted average probabilistic Grad-CAM saliency map to combine the 100 patches back into an overall classification for the original image.

The below image shows the 5 different classes along with the result after the segmentation network.

Data

At the time of the paper, much was still unknown about the COVID-19 illness. So they used the public datasets that were available to them. For the segmentation network, the research team used 247 chest x-rays from the Japanese Society of Radiological Technology (JSRT). They added in another 138 chest x-rays from the U.S. National Library of Medicine (USNLM) during validation. For the classification network, the research team obtained chest x-rays from different sources to total 502 images. Only 180 images were considered to be COVID-19.

Results

The research paper trained their model and compared their success to the SOTA model at the time of COVID-Net. It is worth noting that COVID-Net had a dataset of 15043 images. Oh et al. were able to achieve a sensitivity and precision similar to the SOTA model with 3.3% of the data. Below are the results of the paper.

Technical Contributions

This research team and paper demonstrated some significant technical contributions. They had 1 main approach that is worth mentioning in detail. They addressed their lack of data by using data augmentation to increase their dataset size by 100x. For their model, it resulted in having 502 images to 50,200 images. This is actually even more than the SOTA model. They were able to accomplish this by randomly sampling from the lung area of the chest x-ray. The team also compared their model effectiveness when training with that 50,200 dataset compared to their original 502 image set. In the below image, the original image set is called Global approach and the random patch image set is called the Local approach. We can see that their model achieved significant improvement in all categories.