# Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions
## 1 Introduction  
Deep learning technology has been applied in various situations in the field of image analysis and classification. Medical image analysis is recognized as an important application of deep learning. However, due to the associated high costs (e.g., labeling by domain experts) and complex security aspects of clinical data (e.g., privacy implications and regulation), deep learning-based medical image analysis has not yet been fully adopted in practice. Recently, there have been increasing attempts to develop sample-efficient deep learning techniques and to apply them in the field of medical image analysis where it is difficult to obtain a sufficiently large training dataset. For example, the the authors of [1] employed self-supervised learning (SSL) to achieve high accuracy in skin lesion classification. In general, SSL-based approaches establish pretrained models using unlabeled datasets, whichcan be transferred to a specific task using a small amount of labeled data. Unlike SSL, federated learning (FL) focuses on leveraging labeled data from multiple clients to overcome the data limitation conditions of a single client, while supporting privacy-preserving distributed operations on model training. In [2], the Auto-FedAvg approach is described which addresses strict regulation and privacy issues of clinical data by adopting model federation among multiple medical institutions. In contrast to conventional deep learning, for which locally owned data needs to be integrated into a single dataset for model training, in FL, each institution individually conducts local model training on its own data and shares only its local model with a central server or other institutions. That is, the union of locally trained data corresponds to an entire training dataset; however, union operations are not conducted on training data but, instead, on model parameters among multiple FL clients. There have also been a number of studies utilizing synthetic data for deep-learning-based medical image analysis [3; 4; 5; 6; 7]. In this paper, we apply these sample-efficient deep learning techniques to develop an inference model for burn severity assessment, which achieves high diagnostic accuracy for distinct degrees of severity under limited training dataset conditions. Burns affect many people every year; for instance, it has been reported that about 1.1 million patients suffered from burn injuries in the United States, and that thousands of deaths resulted from burn injuries and burn-related infections [8]. According to Korean national health insurance service records, each year in South Korea, about half a million people receive burn treatment. A burn injury destroys human skin and adjacent tissues and requires suitable clinical treatment depending on its intensity and severity. A timely and correct diagnosis of burn injuries is recognized as an important factor for patient treatment and recovery [9]. For burn severity assessment, we sought to develop an accurate classification system that would enable assessment of burn-related skin images by learning to extract important features from the images, thereby enabling highly accurate and timely diagnosis based on a single image. To achieve such a high-accuracy model, we first built a dataset of skin burn images, which was sufficiently large to produce convolutional neural network (CNN)-based high-accuracy models through conventional supervised learning. From a deep learning process perspective, it is crucial to establish a high-quality, large-scale dataset in which diagnosis information provided by experienced medical practitioners is used as ground-truth labels. We obtained such a dataset of burn images with diagnostic information through research cooperation with one of the largest medical institutions dealing with burns in South Korea. We then trained our target models by adopting sample-efficient deep learning techniques and evaluated the model performance.