# Unsupervised Cyclic Siamese Networks Automating Cell Imagery Analysis
## 4 Results
### Comparison  
We present the results of our method and the comparative baselines in Table 2. The mean relative error (MRE) is a normalized error, taking the ground truth into account, i.e., in high cell count images, small absolute deviations do not increase the error as much as they do for low cell count images. When interpreting experimental results as a biological expert, in most cases, this is the more meaningful indication over the mean absolute error (MAE), which serves as the typical indicator in terms of a regression task. The bilateral alteration SC-VAE-B that uses fully cycled images (back and forth) results in marginal but reliable improvements, assimilating representations in the latent space, and should be considered our top candidate.  
Our SC-VAE consistently outperforms the other state of the art methods Watershed, BiT, and EfficientNet by a wide margin. SC-VAE and its alteration SC-VAE-B correctly estimate around 62 % of the cell counts for the Nat-PC data set, and their predictions differ on average by only 0.5 cells from the true cell counts of the images, and they achieve approximately 5.1 % MRE. For the smaller Nat-BF data set, SC-VAE-B accomplishes 0.56 MAE, 6.5 % MRE, and 58.7 % accuracy. While Dual Transfer Twin-VAE achieves slightly better results for these data, they are attained by semi-supervised training, commonly not even compared to unsupervised methods. As such, Siamese-Cycle-VAE holds up against semi-supervised training methods and even exceeds them the case of the larger Nat-PC data set, making it suitable for reliable cell counting with various microscopy techniques.  
Moreover, we see that Siamese-Cycle-VAE performs well across the entire range of cell counts in Nat-PC and Nat-BF. By contrast, Watershed and EfficientNet struggle with images that contain few cells, which is the most important range of cell counts for biological tasks, such as estimating the growth rate.  
The ablation C-VAE that feeds all data through the same encoder and decoder results in accuracy on synthetic data that is inferior to the other methods, even more so for the important accuracy on natural data. By using the reconstructed images as new input,  
Figure 5: Visualization of the combined losses of SC-VAE top-performing model during training with regularly applied tests, in this case of Nat-PC. It can be seen that after 20,000 epochs, convergence is imminent, but has not fully been reached. Accuracy on cell counts does not improve significantly after this point; only image reconstruction quality does. Since the primary goal is not to diminish the reconstruction and normalization losses to zero, but rather to balance out the different losses, the combined loss can only indirectly be interpreted as a convergence indicator. Nevertheless, larger and faster descents in the combined loss still resemble well-trained models, even if this is insufficient as a sole indicator of such.  
the learning scheme resembles the optimized scheme of SC-VAE in such a way that visual intricacy on natural data is simplified, but not on the same level as SC-VAE.  
S-VAE, on the other hand, worked best on synthetic data, especially so for Nat-BF, but for both types of microscopy data, the MRE and accuracy on the natural data are far from the results from SC-VAE. No translated natural data are generated by S-VAE, which is missing the regression loss for natural data completely. Cell counts on natural data are not random since there is still the shared encoder to unify the two types of data, but since accuracies differ vastly between natural and synthetic data, the S-VAEs encoder fails to do so because of a missing incentive.