# Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions
## 3 Methods
### Self-Supervised Learning  
Similar to TL as previously described, SSL is used to create a pretrained model which can be used as a well-initialized model for further target task-specific learning. However, unlike TL, where the pretraining requires a large number of labeled samples, which are not sufficiently available most of the time in many fields, such as medicine, the pretraining in SSL does not require labeled samples.  
In principle, the SSL-based approach involves a two-stage learning process. In the pretraining stage, a set of unlabeled images is used to build a pretrained model, and in the training stage (i.e., downstream task learning), a set of labeled burn images, with professionally annotated diagnosis information, is used to adapt the pretrained model. Specifically, we tested two different methods that explore SSL: (1) using a publicly available SSL-based pretrained CNN (SSL-inn) and (2) performing SSL from scratch with unlabeled skin images (SSL-burn). Both methods rely on a contrastive visual learning framework simCLR [10] in which a model is first trained through unsupervised contrastive learning with data augmentation and transformation to learn general representations, and then it is fine-tuned on a task-specific labeled dataset.  
In SSL-inn, we used the publicly available pretrained simCLR model based on the ImageNet dataset, which was treated as the starting point for our downstream task learning, burn severity assessment. In SSL-burn, we also created a pretrained simCLR model through contrastive learning on our own unlabeled (unannotated) skin images. This method exploits contrastive loss to learn general representations of skin images through the agreement among differently augmented images from the same input image as well as the distinction among those from different images. In both methods, we used only a small amount of labeled (annotated) burn images to fine-tune the pretrained simCLR model for our task.  
The two methods consider limited data conditions differently in terms of the availability of labeled and unlabeled data. More specifically, SSL-burn is intended to incorporate unlabeled images into the pretraining stage, focusing on commonly observed situations in the field of medicine where many medical images are not annotated but stored as part of medical records. SSL-burn can be seen as semi-supervised learning [27], as it makes use of both a large amount of unlabeled images in representational pretraining and a small amount of labeled burn images in task-specific learning to ensure accurate burn diagnosis capability. As such, SSL-burn is considered suitable when a large quantity of unlabeled images can be used.  
Figure 3 shows the building blocks in simCLR for the case of SSL-burn where unlabeled skin images are used and augmented for contrastive learning. The encoder learns a function that encodes each image into an embedding vector. For an image \(x\), its positive pairs \(x_{i}\) and \(x_{j}\) have similar embeddings, obtaining \(h_{i}\approx h_{j}\). We generate these positive pairs by applying transformation functions listed in Section 3.3 on \(x\). (1) The encoder and project head are learned to minimize their contrastive loss, i.e., the cosine similarity of latent vectors \(z_{i}\) and \(z_{j}\) in our implementation. In this pretraining, negative pairs for \(x\) are also used, which are generated by the same augmentation functions but on different images other than \(x\). The contrastive loss among those samples augmented from different images is also used for learning. After the contrastive learning, (2) the encoder and linear head are fine-tuned on a dataset of labeled burn images through supervised learning to conduct the burn severity assessment task.