# Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions
## 2 Burn Severity Assessment
### Burn Image Dataset  
Burn severity is categorized into different types according to the intensity and depth of burn injury elements: _superficial partial_ thickness, _intermediate partial_ thickness, _deep partial_ thickness, _full_ thickness, and normal. Each type of burn is defined as follows: (1) superficial partial thickness involves the epidermis and superficial dermis; (2) deep partial thickness involves the epidermis and deeper dermis; (3) full thickness burns destroy the epidermis and dermis; and (4) intermediate partial thickness refers to when it is difficult to determine whether a burn injury is superficial partial or deep partial. It is often non-trivial, even for professionals, to distinguish between superficial partial and deep partial thickness, and, in that case, some burn injuries need to be judged again after a certain period of time. Thus, it is important to judge the burn severity as intermediate partial thickness first and to make a more accurate diagnosis later. The samples of different burn types are presented in Figure 1, where different characteristics for the burn types appear in their images. As there is no publicly available large burn image dataset that can be used for training burn-related prediction models, we processed numerous burn images and normal skin images collocated from multi-year burn-related medical records. For these, we collaborated with the Bestian Hospital ([[http://eng.bestianseoul.com/specialized-center/about-the-burn-center/](http://eng.bestianseoul.com/specialized-center/about-the-burn-center/)]([http://eng.bestianseoul.com/specialized-center/about-the-burn-center/](http://eng.bestianseoul.com/specialized-center/about-the-burn-center/)), accessed on 1 June 2022), one of the largest professional burn hospitals in South Korea. Figure 2 illustrates how burn images are professionally annotated with bounding boxes around injuries and their labels in burn types, for which a GUI-based data annotation tool, the _Jonathan Marker_ ([[https://jonathan.acryl.ai/marker](https://jonathan.acryl.ai/marker)]([https://jonathan.acryl.ai/marker](https://jonathan.acryl.ai/marker)), accessed on 1 June 2022) has been used. The tool supports data labeling of different media formats, such as text, images, and video, and enables quality control over data creation pipelines. For each image, a bounding box on the burned area was drawn and labeled by professionals using the tool. For each annotated burn image, a team of three professionals verified its correctness. In this way, we established a high-quality, large dataset of 13,715 annotated burn image samples that were recorded from outpatients and inpatients during the period from March 2013 to April 2019. We also collected normal skin images that had no annotation. Table 1 illustrates the statistics of the dataset; we used 1700 images for each burn type (i.e., normal, superficial partial, intermediate partial, deep partial, and full thickness) in the training dataset and the other images in the test dataset. We intentionally used a subset of the training dataset (i.e., only using 20, 40, 60,..., 170 samples for each burn type) to emulate limited data conditions, as we focus on those problems in practice. Figure 1: Burn samples with different levels of severity: compared to **(a)**_Normal_ skin that has no specific features, **(b)**_Superficial partial_ thickness and **(c)**_Intermediate partial_ thickness appear pink and red, respectively, and furthermore **(c)** appears to have blisters; **(d)**_Deep partial_ thickness appears to involve the epidermis and deeper dermis, including some yellow areas related to the deeper dermis damage; **(e)**_Full thickness_ appears black and dark brown, involving subcutaneous structure-relevant features. Figure 2: Burn data annotation: the burn injury area is bounding-boxed and annotated by professionals; it is classified as deep partial thickness, considering some white regions related to the deeper dermis damage.