# Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions
## 3 Methods
### Learning with Data Augmentation  
To address the limited availability of training datasets for medical images, several image augmentation heuristics have been tested in deep learning frameworks [35]. For our burn image dataset, we tested the following image transform and augmentation operations that are widely used and implemented in [36]. **RandomBrightnessContrast (RBC)** changes brightness and contrast randomly; **RandomGamma (RG)** changes the gamma value randomly; **ColorJitter (CJ)** changes the brightness, contrast, and saturation randomly; **ISONoise (ISO)** applies camera sensor noise; **GridDropout (DROP)** drops out rectangular regions of an image and the corresponding mask in a grid fashion; **RandomFog (RF)** simulates fog on an image; **ElasticTransform (ET)** applies elastic distortion; and **GridDistortion (GT)** applies grid distortion. Recently, GAN-based data augmentation approaches have been explored to create synthetic images in various domains. They aim to increase the size and diversity of training datasets and to mitigate the problems of class imbalance and sample deficiency in training datasets. A vanilla GAN architecture includes two neural networks that are trained through performing a minimax game in an adversarial way. A generator network \((G)\) produces synthetic images (i.e., \(\hat{x}=G(z)\) for random noise \(z\)) and a discriminator network \((D)\) takes those images as input to determine whether they are real or synthetic (i.e., \(D(x)=1\) for real samples and \(D(\hat{x})=0\) for synthetic samples). The \(G\) and \(D\) networks are adversaries in that \(G\) tries to minimize the following minimax (adversarial) loss function while \(D\) tries to maximize it with a maximum log likelihood objective. \[\min_{G}\max_{D}E_{x}[\log(D(x))]+E_{z}[1-\log(D(G(z)))] \tag{2}\]  
By exploiting GAN techniques, very diverse automatic image synthesis is systematically enabled. These techniques are considered promising to reduce the expensive workloads that are required to annotate images professionally, especially for medical image analysis tasks [37, 38, 39]. For burn image augmentation, we tested two well-known advanced GAN techniques, CycleGAN [40] and StyleGAN [41], whose ability to synthesize high resolution images is of great interest in a broad spectrum of medical image augmentation and imaging modalities, e.g., synthesizing cardiac MR images [42], high tissue contrast MR images [43], skin lesion images [6,44], and brain CT images [45]. In the CycleGAN architecture, two GANs, where each GAN consists of a pair of generator and discriminator \(G_{X}\), \(D_{X}\) and \(G_{Y}\), \(D_{Y}\), are chained to support automatic image-to-image translation (i.e., \(X\) to \(Y\) and \(Y\) to \(X\)) without paired image samples between two different domains \(X\) and \(Y\). Figure 4 shows the building blocks of CycleGAN used for our burn image synthesis. Unlike conventional image-to-image translation models (e.g., pix2 pix [46]) that require a dataset of paired samples, such as pairs of source images in \(X\) and their corresponding target images in \(Y\), the CycleGAN architecture additionally employs a cycle consistency loss function [40] to support unpaired image-to-image translation. Using the loss function defined below, the CycleGAN enforces the generators \(G_{X}\) and \(G_{Y}\) learned to minimize the discrepancy between the source image \(x\in X\) (and \(y\in Y\)) and its reconstructed image by the two generators \(x^{\prime}=G_{Y}(G_{X}(x))\) (and \(y^{\prime}=G_{X}(G_{y}(y))\)), providing the automatic translation of input images in the domain \(X\) to target images in the domain \(Y\), and vice versa. \[\min_{G_{X},G_{Y}}E_{x}[\|G_{Y}(G_{X}(x))-x\|_{1}]+E_{y}[\|G_{X}(G_{Y}(y))-y \|_{1}] \tag{3}\]  
In our burn image augmentation scenario, we used normal skin images for the source domain \(X\) to generate synthetic burn images of four different burn types corresponding to the target domains \(Y\), assuming a quite small dataset for those burn types.