# An Intelligent Shooting Reward Learning Network Scheme for Medical Image Landmark Detection
###### Abstract  
As the need for medical services has grown in recent years, medical image critical point detection has emerged as a new subject of research for academics. In this paper, a search decision network method is proposed for medical image landmark detection. Unlike the conventional coarse-to-fine methods which generate bias prediction due to poor initialization, our method is to use the neural network structure search strategy to find a suitable network structure and then make reasonable decisions for robust prediction. To achieve this, we formulate medical landmark detection as a Markov decision process and design a shooting reward function to interact with the task. The task aims to maximize the discount of the received value and search for the optimal network architecture over the entire search space. Furthermore, we embed the central difference convolution, which typically extracts the data invariant feature representation, into the architectural search space. In experiments using standard accessible datasets, our approach achieves a detection accuracy of 98.59% in the 4 mm detection range. Our results demonstrate that, on standard datasets, our proposed approach consistently outperforms the majority of methods. neural architecture search; medical landmark detection; reinforcement learning +
Footnote †: journal: Medical Image Landmark Detection. of landmark locations [8]. In simultaneously classifying and identifying landmarks of abdominal ultrasound images using a single network, [PERSON] et al. presented a multi-task learning (MTL) strategy. They discovered that the training method of the multi-task network outperformed the single-task processing method [9]. [PERSON] et al. implicitly modeled the relationships by a spatial configuration block [12]. Anatomical landmarks are often found close to the margin of a particular anatomical area, and [PERSON] et al. developed a cascaded two-stage U-Net network based on this idea for heat map regression [13]. [PERSON] et al. evaluated the clinically significant correlations between the landmarks and position-constrained landmarks with the clinically significant associations, then generated both heat maps and position-constrained vectors for the landmarks [10]. However, the researchers personally created each of the aforementioned neural network architectures. It would not be best to deploy neural network designs that were manually created. On account of this, neural network architectures that can automatically determine the most effective medical landmark regression under the supervised signal of the pertinent task should be taken into consideration. Manually created regression detection networks with excellent performance need significant skill and a large number of comparing experiments to evaluate the network's relevant parameters. As a result, the majority of researchers presently use pre-existing networks (such as U-Net [11] and ResNet [14]) and build on top of them with different modifications for different tasks. Therefore, [PERSON] et al. presented the neural architecture search (NAS) approach, which intends to automatically search for more appropriate neural network architectures for learning than those created manually by experts [15]. The controller is principally responsible for the reinforcement-based strategy for producing new designs for training and assessing the performance of the search architecture. As a reward for architectural search training, the controller utilizes the accuracy of the search architectures on the validation set. NAS has proven to be quite effective in natural image recognition, and researchers are now adapting it to medical image analysis, such as medical segmentation [16] and medical object detection [17]. Figure 1: Doctors perform linear measurements based on the positions of the annotated landmarks. ANB = \(\angleangle L_{5}L_{2}L_{6}\), the angle between the landmark 5, 2 and 6. SNB = \(\angle L_{1}L_{2}L_{6}\), the angle between the landmarks 1, 2 and 6.