# Unsupervised Cyclic Siamese Networks Automating Cell Imagery Analysis
## 5 Discussion  
We now discuss the limitations of this architecture and state possible revisions to overcome them. During analysis, we found that for very small cells in the natural data, only subpar precision is achieved. Since the working resolution of the architecture is 128 \(\times\) 128 pixels, these cells are barely visible in the downscaled versions of the images and can therefore not yield low error estimations. In future work, the working resolution could be doubled per axis, which requires new layers in the specialized encoders and decoders, but leaves the rest of the architecture unchanged. Alternatively, local crops of quarters of the images could be used, allowing a quasi-double resolution by answering the question of cell count with the sum of 4 quarters. Large and high-contrast light reflections can also be problematic for satisfactory regression. When scaling down an image such as the phase-contrast microscopy on the left in Figure 1, the smaller reflections are merely a single bright pixel in the working resolution, too small to impact the cell count. When these reflections are larger, as with the one on the very left, it can lead to quite high reconstruction losses and cause the architecture to replicate these, although they should be filtered out and ignored. To overcome this, a step in the image preprocessing could be added that seeks this effect and dims the affected area. More elegantly, the reconstruction loss could be capped with local maximums, so that the high deviations that derive from this are not fully accounted for in the training of the network. Further, although the proxy image generator is merely auxiliary content for this work, currently, new microscopy imagery makes it obligatory to find appropriate parameters for the generator, accounting for cell sizes, border brightness, etc. A more sophisticated generator could be able to algorithmically generate auxiliary data automatically from given natural data. Due to the different types of network parts present in the architecture and the resulting loss of Equation (5), it can be difficult to understand the importance of optimization of the different parts of the composite loss. Forcing better reconstructions by setting the according weight factors to high numbers may bring the disadvantage of worse regression, but this is not necessary, because, to some extent, better reconstructions will also help to ensure that the existence of cells is represented in the latent space, which is a major requirement for the regressor to achieve high accuracy. Figure 9: Samples generated from the latent space by inputting noise vectors and deconstructing them with the natural (row 1) and synthetic (row 3) decoder. Appropriate cell imagery can be reconstructed from these; consequently, the latent space meaningfully represents the important information of possible input images for this domain. Adding and subtracting tiny amounts to and from these vectors results in semantically similar images (row 2) with often only one cell more or less, where the cells are slightly larger or smaller and have changed position slightly, while samples from a completely different part of the latent space yield completely different images. The amount of hand-crafting meta-parameters could be reduced by the more extensive use of meta-learning systems, such as a modified regressor for the Gaussian process that we used, to enable the creation of a simple tool that users of a complete solution can utilize for cell counting during live cell imaging experiments. Thus far, alternating between automated meta-learning and hand-crafting with multiple parallel runs with different meta-parameter choices has been utilized to find good parameters quickly. Implementations for the real-time, continuous estimation of cell counts in experiment monitoring would be a practical way to make this architecture and its learning scheme easily usable for biologists.