# Synthetic Medical Imaging Generation with Generative Adversarial Networks for Plain Radiographs
## 3 Results
### Training Dataset Size  
The results of training on varying dataset sizes according to FID score can be seen in Figure 5. FID score performance improved with increasing size of the training dataset, though, notably, after the dataset size increased to 200+ images, improvement in the FID score was fairly marginal with increasing dataset size. Images were generated during training at every 20 th training iteration, and the generated images at the point of convergence were inspected for artifacts. The smaller training datasets (500 images or fewer) produced images with artifacts present. Some examples of these artifacts can be found in Figure 6. We can see non-straight edges and lack of the ball and socket joint in images produced when trained with 500 images. We can see the start of another X-ray and unrealistic curved bone in images produced when trained with 400 images. We can see the lack of a ball and socket joint, curved bone, and unrealistic truncating of bone in images produced when trained with 300 images. We can see the lack of a ball and socket join in images produced when trained with 200 images. We can see a bone joining to another bone rather than the joint, as well as the start of another X-ray in images produced when trained with 100 images. However, the run trained on 781 images produced no artifacts. Therefore, in this case, the FID score alone was not sufficient for determining the quality of the images, as the smaller datasets converged to a similar (albeit larger) FID score than the largest dataset of 781 images. For lateral elbow X-rays, a threshold training dataset size of greater than 500 images is required to generate realistic, artifact-free images. It should be noted that the complexity of images will vary across the anatomy being X-rayed, as well as the view of the X-ray. Thus, this threshold is meant to serve as a rough baseline for lateral elbow X-rays but should not be construed as applicable to any X-ray type. Additionally, one could further decrease the minimum threshold required if they separated the different lateral elbow X-rays into classes based on similarity (for example, the size of the X-ray in the image).  
This analysis is further supported by the results from leveraging transfer learning. Performance of each model that continued training from the base model improved with dataset size. The results can be seen in Figure 7. In the figure, we can see that each time a smaller dataset was used, the generated images included artifacts. This denotes inferior performance, especially when compared with the base model trained on 781 images, whose results can be seen in Figure 3(a2,b2,c2). This suggests that in order to generate images free from artifacts, a model will need at least 750 images for training.  
Figure 4: Results of hyperparameter tuning.  
Figure 5: FID score results of varying training dataset sizes.  
Figure 6: Artifacts discovered when training smaller datasets. Each image indicates the number of training images used to train the model which produced the image. In each image the dashed lines and dashed circles demonstrate the noted artifacts and improvements.  
Figure 7: Results of transfer learning using smaller datasets.