# Unsupervised Cyclic Siamese Networks Automating Cell Imagery Analysis
## 4 Results
### Image Reconstruction and Representation  
An analysis of the reconstruction abilities of Siamese-Cycle-VAE is useful to ensure that the shared representation is meaningful, even though our main aim is automatic cell counting, not perfect image reconstruction.  
During the training of Siamese-Cycle-VAE, the image inputs are processed by their respective encoder, followed by the general, weight-shared encoder, represented in the bottleneck of the architecture; they are then processed by the shared decoder and finally reconstructed by their specialized decoder accordingly (see Figure 4). The same is true for auxiliary data and both types of translated pseudo-imagery. To ensure that the actual regressive task works as intended for natural images, it must be able to benefit from synthetic data representations in the latent space, so the learned representation must be shared by the four types of data.  
This can be verified by encoding natural images with their appropriate encoder, but performing the decoding with the decoder that is designed and trained for auxiliary images, the counterpart to the opposite conversion, which is done in every epoch of training. Minimal changes in the stages of the images that are converted back and forth indicate the close coupling of the representations. The closer the different data types are transformed into the latent space, the greater the potential gain for regression on natural data. Moreover, the conversion makes this fact interpretable on a visual level.  
We show examples of perfect translations in Figure 6. For these samples, a natural image is encoded and then decoded as a synthetic image. The number of cells remains unchanged, and the position and size of the cells are also maintained. However, the overall appearance is simplified: Siamese-Cycle-VAE learned to remove noise and to break down the reconstruction to the essentials. Even the very large smudge on the left natural image has not been reconstructed; although it will cause an increased loss in the reconstruction, the weighting of the loss factors makes it more acceptable to forfeit image reconstruction precision in favor of the regression. On the right side, it can be seen that the output does indeed appear more similar to natural data than the synthetic input does, while fine details such as the noisy borders are not recreated.  
The ongoing cell division shown in Figure 7 is a prime example to understand how Siamese-Cycle-VAE works. The membrane of the bottom right cell is not fully enclosed and there is no overlap, since a fine bright border of the underlying cell would be seen through the top cell. However, two cell cores can clearly be seen and a human expert would presumably count this situation as two cells, which is exactly what Siamese-Cycle-VAE does. The prediction of 9.65 instead of 10 can be understood as uncertainty and a slightly earlier stage of the division would have arguably led to a slightly smaller prediction, which, when rounded, would be the correct cell count again. The effect of simplified visuals also happens in these non-translated reconstructions; the smudges on the Nat-BF sample are clearly fainter and, in the left image, even the high-contrast dead cell residue on the left is not recreated. This clearly indicates that even when Siamese-Cycle-VAE does not predict the cell count perfectly in an image, the comparison between the original and reconstructed image is useful to understand where an error occurs.