acquiring data for the training stage results in limited generalization of the model.
There are different factors that cannot be fully addressed by the data augmentation method. Some examples of these variations are the characteristics of the X-rays depending on the radiological equipment, the differences in the anatomical structure of the pelvis between patients, and the position adopted by the patient when taking the X-ray. In this sense, if there are X-ray images that have not been seen in the training stage, the model does not perform a reconstruction similar to the input image;
and therefore, the removal, detection and localization of the anomalous region is not accurate.
Evaluation Metric.- Another important factor contributing to the limitation of the proposal is the absence of a unified evaluation metric that assesses the performance of a generative adversarial network in anomaly identification. As we saw in the review of the state of the art, some authors only present qualitative results. Other authors present quantitative results, but differ by opting for an anomaly score based on the generator or discriminator errors of the generative adversarial network. Additionally, some of the quantitative results presented describe that they measure the visual quality or realistic nature of the reconstructed images. However, the exact definition of “visual quality” or “realistic nature” is rarely provided. We believe that there is a need for the development of a quantitative evaluation metric that validates the performance of a generative adversarial network considering qualitative aspects as well.
6.4 Future Work
Our future work is mostly focused on the synthetic data augmentation proposal, the unsupervised anomaly detection method, and the analysis of results. These future lines are presented in more detail below.
Synthetic Data Augmentation.- Proposing a data augmentation strategy that contemplates a wider range of population is necessary to improve the reconstruction stage. Therefore, it is of interest to propose a data augmentation method that considers multiple variations in the shape and texture of pelvic X-ray images. Considering that synthetic data are useful for the training stage and for obtaining more accurate results, a proper generation of synthetic X-ray images may result in the publication of a pelvic structure dataset for future research.
Unsupervised Anomaly Detection.- Our future work will be focused on the incorporation of an attention module that captures global and local information from the X-ray images. We believe that a self-attention module with positional information and memorization will be useful to capture and store information about the image
CHAPTER 6. Conclusion and Future Work
structure. In this regard, we can recognize important details that are inherent to the pelvic bone in the learning process. Then, in the inference stage, we can better detect if an X-ray image presents anomalies or not. Therefore, if the model learns fine details about shapes, intensities, borders and dimensions, the reconstruction stage will be improved, and consequently the localization of anomalies.
Analysis of Results.- We would like to improve the quantitative analysis of our results by including the proposed approach in a framework that performs the 3D reconstruction task. In this regard, the experts may validate whether the 3D models obtained from X-ray images with anomalies are adequate.
Master Program in Computer Science - UCSP 81
6.4. Future Work
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