## 2 System Description  
As shown in Figure 1, a clinical psoriasis skin image often has normal skin regions, psoriasis areas, and a simple background. Consider an input image of size \(N_{W}\times N_{H}\), where \(N_{W}\) and \(N_{H}\) represent the width and height of the input image, respectively. The goal of the proposed scheme is to separate some objects or instances (normal skin regions and psoriasis areas) from the background in an unconstrained image. Then, these instances are classified into three categories: Normal \(C_{n}\), psoriasis \(C_{p}\), and background \(C_{b}\). This means that each pixel of an unconstrained image will be assigned with one of the class information (\(C_{n}\), \(C_{p}\), and \(C_{b}\)) after image segmentation. Generally, an instance segmentation method can be divided into two parts: object detection and pixel classification. Currently, the instance segmentation methods are usually developed based on object detection algorithms such as single-shot detector (SSD) [22] and Faster Region-Based Convolutional Neural Networks (Faster R-CNN) [23]. According to the different types of object detection architecture, instance segmentation methods can be divided into two categories: Single-stage [24; 25] and two-stage [26; 27; 28]. As for the two-stage category, these instance segmentation methods are developed based on existing two-stage object detection algorithms. For example, Mask R-CNN [24] is an extension of Faster R-CNN. To achieve instance segmentation, Mask R-CNN is composed of backbone network, Regional Proposal Network (RPN), feature pyramid network (FPN), ROIAlign, and FCN. The backbone network composed of several convolutional layers is utilized to extract multilevel feature maps from an image, and FPN is designed to generate multiscale feature maps for effectively classifying and localizing objects with multiple sizes. RPN is used to find regions of interest (ROIs) from these multiscale feature maps. ROIAlign is exploited to reduce the misalignment of an object position between the feature map and the spatial coordinate. The last key component, the FCN network, is used to find the precise boundary of each ROI. Another existing method [25] predicts a set of position-sensitive output score maps which simultaneously address object classes, boxes, and masks. The above state-of-the-art two-stage methods can achieve satisfying performance, but they are time-consuming. As for single-stage instance segmentation, the existing methods are usually proposed based one-stage object detection methods. Polarmask [27] formulates the instance segmentation problem as instance center classification and dense distance regression in a polar coordinate. SPRNet [28] has an encoder-decoder structure where classification, regression, and mask branches are processed in parallel. SPRNet generates each instance mask from a single pixel, and then resizes the mask to fit the corresponding box to gain the final instance level prediction. In the decoding part, each pixel is exploited as an instance carrier to create the instance mask on which consecutive deconvolutions are applied to gain the final predictions. The authors of [26] proposed an instance segmentation network, You Only Look At CoefficientTs (YOLACT), for real-time applications. Compared with the existing image segmentation methods, YOLACT is more efficient because one-stage object detector is adopted as the base. As mentioned in Section 1, the goal of the proposed instance segmentation scheme is to effectively detect and identify psoriasis regions and normal skin areas in clinical psoriasis images. Currently, most deep learning frameworks are developed to deal with natural images, and only a few deep-learning-based frameworks are designed for psoriasis images [12; 21]. Though these existing learning-based segmentation methods are developed for dealing with natural images, they can be modified to develop a CAD scheme for medical images.