# Accurate Landmark Localization for Medical Images Using Perturbations
## 5 Discussions and Conclusions  
The performance improvement by the perturbations can be explained by the fact that a perturbation functions as a regularizer with data augmentation. Over the entire training phase, the sizes and positions of the patches are chosen randomly, and each training sample is different from one another. The various perturbation parameters described in Section 3.3 enforce further augmentation effect. We suggested several perturbation algorithms and tested them. The perturbation operations cause a loss of the local information of the image. This forces the models to search for global features. The perturbations are different in the amount of information loss. Figure 7 shows the diverse attributes of the perturbators. The blackout and whiteout perturbations erase all local information. Thus, the network has no choice but to localize the landmarks by learning the global features outside the patch. Smoothing reduces the information in the patch, so the network can rely both on the inside and outside of the patch. Binarization and edge detection have two opposite facets, weakening or strengthening the local information of the patch. The weakening facet happens because the details of the patch disappear. The strengthening facet happens because the operations sharpen the region boundaries. So, binarization and edge detection can be regarded as guiding the feature learning.  
The guided feature learning, it is worth noting, is an effective way of enriching the feature representation. Many researchers showed that the guided feature learning improved the accuracy of object detection. A notable result can be found in [43] where the dilated convolution layers played the role of the feature guiding. In that work, the feature guidance is learned in the end-to-end manner. On the contrary, our perturbators accomplish the feature learning by using hand-crafted features as a preprocessor. One of our important future works is to embed the perturbation module in the network architecture and learn the perturbator automatically. This extension is expected to improve the accuracy of landmark localization. Another benefit comes from removing the necessity of manually setting the parameters in Table 2.  
In Tables 3 and 4, the edge detection that plays the role of the guided feature learning generally leads other operations. The hybrid that randomly chooses edge detection also resulted in significant improvement. These improvements have a positive effect on planning treatment for dentists. Geometric features such as distances and angles are used to classify the patients into several groups. Accurate classification is very important for the treatment. We argue that the accuracy improvement by the edge detection and hybrid perturbators has a significant impact on building a dental treatment system.  
The most important future work is to apply the perturbators to other medical tasks described in Introduction such as analyzing features of the humerus and aortic valve localization for pre-operative surgical planning.  
Conceptualization, J.K., I.-S.O.; methodology, J.K., K.O.; software, J.K., K.O.; validation, J.K., K.O., I.-S.O.; formal analysis, J.K.; investigation, J.K., K.O.; writing-original draft preparation, J.K.; writing-review and editing, J.K., K.O., I.-S.O.; supervision, I.-S.O.; project administration, J.K., I.-S.O. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT), funding number (No. 2019R1F1A10635221).  
Figure 7: Attribute of perturbators. Blackout and whiteout completely erase the local information. Smoothing reduces the local information. Binarization and edge detection can guide the network to learn both the local and global features.