## 2 System Description
Unfortunately, it may be difficult to collect many medical images and corresponding labeling information for training deeper networks. On the other hand, transfer learning [20; 29] is to transfer the knowledge from a related domain (source domain) to increase the performance in a specific domain (target domain). It is expected that transfer learning is a time-saving approach for building a machine-learning-based method when only a small training dataset is available in real applications. Therefore, an instance segmentation scheme based on single-stage instance segmentation can be developed via transfer learning for psoriasis images. Figure 2 shows the methodological illustration of the proposed scheme via transfer learning. As shown in Figure 2, a pretrained deep neural network is selected from the source domain and can be retrained on the target domain to generate a CAD system via  
transfer learning. In real applications, the source domain \(\text{D}^{\text{S}}\) with the corresponding task \(\text{T}^{\text{S}}\) is often collected as follows:  
\[\text{D}^{\text{S}}=\Big{\{}\left(x_{1}^{\text{S}},y_{1}^{\text{S}}\right), \left(x_{2}^{\text{S}},y_{2}^{\text{S}}\right),\cdots,\left(x_{ns}^{\text{S}},y _{ns}^{\text{S}}\right)\Big{\}}, \tag{1}\]  
where \(x_{i}^{\text{S}}\) and \(y_{i}^{\text{S}}\) denote the \(i\)-th instance and the corresponding label information in \(\text{D}^{\text{S}}\), respectively, and \(ns\) means the number of instances in \(\text{D}^{\text{S}}\). For example, there are some popular datasets such as ImageNet [10] and MSCOCO [30], and these datasets are used for computer vision tasks such as image classification, object detection, image segmentation, and so on. For instance, in segmentation, the label space \(Y^{\text{S}}=\{y_{i}^{\text{S}}\}\) contains the class, the bounding box, and the mask information of the \(i\)-th instance in \(\text{D}^{\text{S}}\). Similarly, the data in the target domain \(\text{D}^{\text{T}}\) with the corresponding task \(\text{T}^{\text{T}}\) are mentioned below:  
\[\text{D}^{\text{T}}=\Big{\{}\left(x_{1}^{\text{T}},y_{1}^{\text{T}}\right), \left(x_{2}^{\text{T}},y_{2}^{\text{T}}\right),\cdots,\left(x_{nt}^{\text{T}},y_{nt}^{\text{T}}\right)\Big{\}}, \tag{2}\]  
where \(x_{i}^{\text{T}}\) and \(y_{i}^{\text{T}}\) represent the \(i\)-th instance and the corresponding label information in \(\text{D}^{\text{T}}\), respectively, and \(nt\) is the number of instances in \(\text{D}^{\text{T}}\). Here, the target task \(\text{T}^{\text{T}}\) is instance segmentation for psoriasis images. Then the following problem is how to efficiently learn a network model with high accuracy for \(\text{T}^{\text{T}}\) according to \(\text{D}^{\text{S}}\) or trained models in \(\text{D}^{\text{S}}\). Figure 2: Methodological illustration of the proposed scheme. To date, some approaches such as model finetuning, multi-task learning, and domain-adversarial learning have been developed in transfer learning [20, 29]. As for model finetuning, layer transfer is a common approach for network-based methods. Since the proposed psoriasis segmentation scheme is a network-based method, as shown in Figure 2, layer transfer is then a suitable approach to train the network model in the proposed scheme. In addition, it is worth mentioning that overfitting may happen if a small dataset is used to train a deep neural network. Unfortunately, \(nt\) may be less than \(ns\) in some applications, e.g., medical image analysis and computer-aided diagnosis. As we know, data augmentation is a very powerful method to prevent the model overfitting issue [31]. Due to the limited size of psoriasis images, data augmentation was adopted to enlarge the size of the training image dataset here.