# An Intelligent Shooting Reward Learning Network Scheme for Medical Image Landmark Detection
## 5 Conclusions and Future Work  
In this paper, we develop a reinforcement learning approach and a learning reward to choose a reliable landmark regression network architecture. Extensive experimental results are presented in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **2.0 mm (SDR\%)** & **2.5 mm (SDR\%)** & **3.0 mm (SDR\%)** & **4.0 mm (SDR\%)** \\ \hline [PERSON] et al. [50] & 66.11 & 72.00 & 77.63 & 87.43 \\ [PERSON] et al. [51] & 62.74 & 70.47 & 76.53 & 85.11 \\ [PERSON] et al. [52] & 67.68 & 74.16 & 79.11 & 84.63 \\ [PERSON] [53] & 69.89 & 78.95 & 85.16 & 92.32 \\ [PERSON] et al. [8] & 76.00 & 82.9 & 88.74 & 94.32 \\ [PERSON] et al. [13] & 75.05 & 82.84 & 88.53 & 95.05 \\ [PERSON] et al. [10] & **80.42** & 87.84 & 89.68 & 94.63 \\ [PERSON] et al. [31] & 76.57 & 83.68 & 88.21 & 94.31 \\ [PERSON] & 79.05 & **87.95** & **89.79** & **95.05** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparisons of SDR of our method with recent compelling methods on ISBI2015 test set (2). Figure 10: Mean radial errors (unit: 1 mm) in detection of individual landmarks on the ISBI2015 Test (1). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **MRE** & **2.0 mm (SDR\%)** & **2.5 mm (SDR\%)** & **3.0 mm (SDR\%)** & **4.0 mm (SDR\%)** \\ \hline Baseline-1 & 1.3 & 84.03 & 90.87 & 93.89 & 97.29 \\ Baseline-2 & 1.27 & 85.5 & 91.32 & 94.00 & 97.96 \\ Baseline-3 & 1.23 & 86.12 & 91.57 & 94.84 & 98.02 \\ Ours & 1.09 & 87.87 & 92.45 & 95.54 & 98.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study results of Baseline-1, Baseline-2, Baseline-3 and our shooting reward learning in ISBI2015 test set 1 (measured by MRE and SDR). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **MRE** & **2.0 mm (SDR\%)** & **2.5 mm (SDR\%)** & **3.0 mm (SDR\%)** & **4.0 mm (SDR\%)** \\ \hline Baseline-1 & 1.58 & 71.26 & 80.36 & 85.94 & 93.73 \\ Baseline-2 & 1.53 & 73.63 & 81.84 & 87.78 & 94.57 \\ Baseline-3 & 1.46 & 74.21 & 83.47 & 88.10 & 94.63 \\ Ours & 1.34 & 79.05 & 87.95 & 89.79 & 95.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study results of Baseline-1, Baseline-2, Baseline-3 and our shooting reward learning in ISBI2015 test set 2 (measured by MRE and SDR). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **MRE** & **2.0 mm (SDR\%)** & **2.5 mm (SDR\%)** & **3.0 mm (SDR\%)** & **4.0 mm (SDR\%)** \\ \hline Baseline-1 & 1.58 & 71.26 & 80.36 & 85.94 & 93.73 \\ Baseline-2 & 1.53 & 73.63 & 81.84 & 87.78 & 94.57 \\ Baseline-3 & 1.46 & 74.21 & 83.47 & 88.10 & 94.63 \\ Ours & 1.34 & 79.05 & 87.95 & 89.79 & 95.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparisons of SDR of our method with recent compelling methods on ISBI2015 test set (1). results on standard benchmarks show the validity of our shooting reward learning network on model balancing and its effectiveness of medical landmark detection. Future research will focus on self-supervised or unsupervised landmark recognition in medical images, which can solve the problem of expensive labeling. To raise the competence of our model, it would be desirable to design new network configurations and operators. Additionally, to provide supervision signals and improve model performance, structural information connecting medical landmarks will be introduced. We may conclude from our experiments that our model still falls short in terms of the regression accuracy of particular landmarks, and the following work might be examined and adapted for specific spots with inadequate identification. Writing--original draft, F.F. and K.H. ; Writing--review & editing, F.F. and K.H. All authors have read and agreed to the published version of the manuscript. This research was funded by Ningxia Natural Science Foundation Key Project grant number: 2021 AAC02004 and Ningxia Key Research and Development project grant number: 2022 BEG02016. Not applicable.