# An Intelligent Shooting Reward Learning Network Scheme for Medical Image Landmark Detection
## 2 Related Work  
In this subsection, we briefly review the related literature on landmark detection. **Conventional landmark detection. ** Image landmark detection is widely used in face-alignment tasks. In face-alignment research, cascaded regression is widely used to map landmark feature localization to shape. In the initial detection of landmarks in medical images, methods such as pixel classification and random forest regression are mainly used. These methods utilize prior knowledge in statistics to be data driven to explore mathematical relationships between landmarks. However, there are some problems with both face-alignment and landmark-detection methods in medical images. Essentially, the features of images are extracted manually, which requires professional experts and consumes a lot of time. Furthermore, the mapping of linear features cannot handle the localization of landmarks in complex scenes, and the prediction stability of these methods is relatively poor. Hence, proposing a stable and robust method for landmark detection is an extremely challenging task. **Deep Learning-based landmark detection. ** Deep learning networks have been used to address the issue of landmark recognition in recent years [18], mostly in an end-to-end manner with better nonlinear capabilities for image-to-shape mapping. Deep neural networks learn adaptive feature representations directly from raw image pixels and greatly improved model robustness. For example, [PERSON] et al. proposed a deep cascade convolutional network, which, with the powerful feature extraction ability of a convolutional neural network and improved the detection accuracy of landmarks [7]. [PERSON] et al. [19] applied a deep learning network to cephalometric landmark detection for the first time, which proposes an end-to-end deep learning system for cephalometric landmark detection. The landmark detection methods based on deep learning can be divided into those based on coordinate regression, heatmap regression, and graph network regression. [PERSON] et al. [20] presented cascaded pose regression (CPR) which gradually refines a specified initial prediction value through a series of regressors, where each regressor relies on the output of the previous regressor to perform a simple image operation. The whole system can automatically learn from the training samples. [PERSON] et al. [21] proposed a multi-task cascaded convolutional network (MTCNN) that can handle both face detection and alignment. MTCNN adopts an online selection method to improve network performance, which can select difficult samples during the training process. However, these methods provide less supervision information during the training process, and the model converges slowly. To address the problem, heatmap-based approaches [10; 13; 22; 23] have also been proposed for landmark detection on a large scale. The deep learning network based on heatmap directly regresses the probability of each class of landmarks, providing supervision information. The network converges faster, and at the same time, predicting the position of each pixel can improve the positioning accuracy of landmarks. [PERSON] et al. proposed a new heatmap-based cascaded deep neural network (DAN), which can effectively overcome the problems caused by pose changes and initialization by taking the entire image as input. Since the U-net is widely used in the field of medical image processing, the landmark detection of medical images uses the U-net network to predict the landmark heatmap and then process the final landmark position. [PERSON] et al. [24] implemented a multi-task U-net to predict both heatmaps and offset maps simultaneously. [PERSON] et al. [25] developed a general anatomical landmark detection model to implement hybrid-based end-to-end training for multiple landmark detection tasks, and the model design requires much fewer parameters than models with standard convolutions.