## 2 Related Work
### Segmentation  
The purpose of image segmentation is to classify all pixels into a specific class [61]. Predicting all pixels is also called pixel-level classification. In the medical field, image segmentation includes organ segmentation and lesion segmentation, which are used in studies to analyze specific shapes or volumes. Recently, various deep learning studies that performed well with little data have been proposed in the medical image segmentation field. For example, ref. [62] introduced a CNN-based end-to-end model with 2700 wound images with a 47.3% IoU. Ref. [63] also created a CNN model, but when the given dataset was small, data augmentation was performed using color change, resulting in a 53% IoU. In 2017, ref. [64] proposed automated segmentation of diabetic foot ulcers and their surrounding skin using a fully convolutional network (FCN). The FCN-16 model using transfer learning performed best, with a 79.4% DSC. However, while the ground truth had a smooth curve, the predicted segmentation had low accuracy due to irregular boundaries. Ref. [65] presented a 3D CNN-based classification framework in which various pathways existed with a pressure ulcer image. The model trained with 193 images resulted in a 92% DSC and 95% area under the curve (AUC). Ref. [66] presented various feature extraction approaches based on color and text and classified four wound tissues, resulting in an average accuracy of 96%. Ref. [67] experimented with variant segmentation models, and the authors studied the UNet with the VGG19 pre-trained on the ImageNet dataset. Ref. [68] proposed an image segmentation framework, WSNet, which leverages wound domain adaptive pre-training on unlabeled wound images and adopted a global-local architecture to learn fine-grained features. Ref. [69] introduced FANet with an edge feature augment module and a spatial relationship feature augment module to utilize the edge information and the spatial relationship between the wound's and the skin's regions. Also, refs. [57; 70] applied a Mask R-CNN model, but [57] adopted a mesh rasterization method to the model to segment the pressure ulcer. Ref. [71] used a YOLOv5 model to segment and classify pressure ulcers. Ref. [72] experimented with their anatomical structure segmentation method using a CNN with various training techniques, including exponential linear units, batch normalization, and dropout. Moreover, ref. [73] proposed an unsupervised domain adaptation framework based on adversarial networks. The authors divided the method into two parts, a discriminator and a segmentor, and used ResNet18 and FCN, respectively. Ref. [74] tested their method on two tasks, eye vasculature segmentation and neuron membrane detection. The authors proposed two networks, FCN for segmentation and CNN for classification on the segmentor outputs, and connected the networks through a gradient reversal layer for adversarial training. Also, studies using the transformer model have been introduced. Ref. [75] presented a U-shape transformer network for cardiac segmentation and [76] introduced the class-aware adversarial transformer model. Variant UNet models including H-DenseUNet, UNet3+, and nnUNet have been studied for improving the performance of segmentation and then applied to brain tumor, heart, liver, and lung segmentation tasks [77; 78; 79]. Ref. [80] combined UNet and swin transformer models for brain tumor segmentation. Ref. [81] introduced 3D-UNet and SegResNet, combined with advanced fusion techniques to fuse PET and CT information for tumor segmentation. As for recent works, refs. [82; 83] proposed AIM-UNet and CCLnet with cascaded context modules and ladder atrous spatial pyramid pooling, respectively, to segment liver tumors. Ref. [84] experimented with the ResNet model, while [85] utilized the U-Net model on the BraTS 2020 dataset. Related works in the medical imaging domain are examined and organized into three parts: classification, detection, and segmentation.