# Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions
## 3 Methods
### Federated Learning  
While deep learning is promising for image recognition in many fields, existing deep learning models have, to date, only had limited application to medical images. Each medical institution (e.g., hospital) manages its own medical records and rarely shares the datasets with other institutions due to strict regulatory and privacy requirements as well as the expensive process of expert-involving annotation. This data-silo structure makes  
Figure 3: Burn severity assessment based on simCLR [10]: the upper procedure marked with 1� denotes self-supervised pretraining on unlabeled images, and the lower procedure marked with 2� denotes task-specific supervised fine-tuning on labeled images.  
it difficult to establish a sufficiently large dataset of training samples that are needed to leverage traditional deep learning model architectures [28, 29].  
FL is intended to leverage the benefits of deep learning based on large datasets, whilst meeting privacy-preserving requirements, by locally training a model without sharing data among multiple learning clients. For example, in mobile device computing, numerous mobile clients participate in the FL process to establish a high-performance global model without sharing the locally collected privacy-sensitive data at the client side with others [30, 31]. Recently, n the field of medical image analysis, several studies have demonstrated the feasibility of use of FL in various contexts, e.g., medical image segmentation [2], COVID-19 screening [32], skin lesion classification [33], and paediatric chest X-ray classification [34].  
We applied FL to burn severity assessment, assuming a clinical system environment in which each burn medical institution has only a small number of fully annotated burn images and is able to train a model locally with its burn images. Specifically, we used a well-known FL algorithm, FedAvg [31] in which a locally trained model by each FL client (each medical institution) is aggregated via a central server that conducts weighted averaging on the parameters of the models of FL clients. In FL, model aggregation from multiple clients is iteratively conducted for continual model updates, where each aggregation period is called a _round_.  
Consider an FL process with \(K\) clients. At round \(t\), a server sends its global model (parameters) \(w^{t}\) to the FL clients, which is updated on local data by each client \(k\) where \(k=1\),..., \(K\). Then, locally updated models \(w^{t+1}_{k}\) are sent back to the server so that an averaging operation is performed as  
\[w^{t+1}=\frac{1}{K}\sum_{k=1,\,...,\,K}w^{t}_{k} \tag{1}\]  
where we assume the same number of epochs and the same number of training samples across FL clients for simplicity.  
With this FL process, the data is not shared among multiple medical institutions and remains distributed, but the locally trained models are shared. When exploiting their own data for model training, this allows medical institutions not only to migrate the risk of security breaches from a technical perspective but also to ensure data asset protection from a management perspective. Moreover, it enables the generalization of trained models alongside more data samples from multiple medical institutions and less restrictive diversity from multiple regions and demographic groups.  
In our experiments, we deliberately split our burn image dataset into multiple partial datasets to emulate a clinical environment in which the FedAvg algorithm can be adopted for medical institutions with local data. A model was individually learned on local data at each institution; all the model parameters were locally updated at each round and were then globally aggregated and averaged through a cloud-based federated service to obtain a global model with high accuracy.