# Unsupervised Cyclic Siamese Networks Automating Cell Imagery Analysis
## 2 Related Work  
In the last few years, convolutional deep neural networks have become the state of the art for image processing that does not require human labor and for the majority of other computer vision tasks [7]. Especially for the task of counting in images, solutions have been worked on for over a decade now (see [8]). Applications in the biomedical domain have become common [9] and cell tracking approaches in images have been an ongoing field of study in recent years [10]. However, the optimization of such methods is often time-consuming and remains prone to errors. [PERSON] et al. [11] propose a benchmark suite to compare different imaging technologies and extrapolate the strengths and limitations of different approaches to cell tracking, none of which have been determined as a final solution on this task, even the ones including interactions among bioimage analysis experts [12] or the distributed work of manual labeling [13]. [PERSON] et al. [14] show the demand for fleshed out solutions by evaluating  
Figure 1: Samples from the data sets of CHO-K1 suspension growth. Bright-field microscopy image on the right, phase-contrast microscopy image on the left. Smudges on the chip can be seen in the form of faint, small circles within the fluid solution. The scale bars do not appear in the working data. the currently used state of the art tools as insufficient for heterogeneity studies of the CHO-K1 mammalian cells that are present in the given data. In addition, [PERSON] et al. [15] used transfer learning to predict microscope images between different imaging technologies, but without sufficiently accounting for the wide variety of cell images and features. The approach by [PERSON] et al. [16] provides one of the few toolboxes for cell tracking, albeit for adherent rather than suspension cells. It allows for transfer learning based on given models and novel data, whereby data set enrichment technologies limit the number of required samples. In contrast to adherent cell lines, where already reported single cell cultivation studies [17; 18] promise success, we address the more complex scenario of suspension cells with all their visual characteristics listed above, rendering analysis tools of adherent cells deficient. Earlier works have overcome some of the challenges, such as sufficient counting accuracy, by interactive design [19], or detecting overlapping instances in such imagery [20], but they are not yet sufficient for the unsupervised task at hand. Different contrast and light conditions have been addressed by [PERSON] et al. [21]. The adherence of cells and overlaps have been addressed by [PERSON] et al. [22], but additional visual features complicate the process and reduce the applicability of previous solutions. Siamese networks have been used for a variety of tasks as they can help to facilitate few-shot learning or clustering of the data space by generalizing from unlabeled data. This is done in [23] for genome sequencing and in [24] for text data. These presented architectures are, however, specific to their domains and not applicable to image processing. There are also Siamese networks that do work in the image processing domain, such as [25], but they focus on change detection as a binary segmentation, suitable for tracking single cells, but not for the regression task at hand. Ref. [26] uses Siamese networks and data augmentation, similar to our approach, but the training is supervised and addresses a four-class classification task. In [27], similar data augmentation and Siamese networks were used and the 20-class classification is closest to the regression task that we address, but the networks used are non-generative CNNs and the data are not used cyclically, rendering it not applicable for our work.