# Unsupervised Cyclic Siamese Networks Automating Cell Imagery Analysis
## 1 Introduction  
Single cell cultivation is one of the most important steps in single cell analysis [1] and represents an essential means to better understand cell functionality from cellular and subcellular perspectives for diagnosis and therapy, and microfluidic devices constitute fast-rising systems for efficient single cell cultivation. However, the analysis of microfluidic single cell cultivation (MSC) microscopic images is usually performed manually or supported by technological aiding systems, but requires the work of human experts because of the high spatial and temporal resolution and a variety of visual characteristics that make automation difficult. Flexible image processing pipelines have proven their relevance for certain setups, but are limited to specific scenarios and partially interactive, as the fully automated analysis of non-adhesive cells in the presence of the varying light conditions and various artifacts of microscopic images is challenging [2]. In recent years, the potential of deep convolutional architectures for automated and flexible image analysis has been demonstrated in this area, but training procedures for current deep architectures rely, at least partially, on manually labeled training data [3, 4]. A manual procedure is not practical in many applications, creating a demand for effective, fully automated solutions [5]. Therefore, the particular focus of this work is to eradicate the human expert requirement for annotations completely. Henceforth, we will focus on a relevant generic learning task for MSCC image analysis: the cell count is used as the target variable, which has to be estimated reliably at any point in time of the experiment and is chosen mainly for two reasons: (1) it allows for theextrapolation of other important attributes of the experiment, such as the growth delta over the last few time segments, as well as the overall growth rate, and (2) as a regression task, it is known to be especially difficult to be estimated accurately for unsupervised training methods, i.e., it can be inferred that tasks that are generally considered more simple, such as classification or segmentation, can also be solved with the methodology presented in Section 3. In the following, we will aim for a solution that does not rely on any manually generated labels. Instead, we will rely on automatically generated artificial labels, i.e., use \"fully automated supervision\". To prevent misunderstandings, unsupervised deep learning would, in its most exclusive definition, not be able to solve the addressed task, since the lack of labels means that the regression loss cannot be calculated. Therefore, we refer to \"unsupervised learning\" for this task as the absence of manually curated labels for the experimental data. There needs to be a computable loss on the target variable to achieve actual training, which, in our case, can explicitly and efficiently be defined, based on the available symbolic semantics for auxiliary synthetic data. Even self-training architectures such as Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) can only generate losses on predictions and reconstructions of the data, not on the target variable. The Siamese-like architecture described later will therefore not only train on natural data, created by the biotechnological experiments, but also on a collection of synthetic auxiliary data with automatically generated labels and therefore known ground truth. By training this architecture with a special learning scheme, it is not only possible to perform regression learning on the target variable, but also to achieve accuracy that approaches or, in some cases, exceeds the state of the art (see Section 4). While our own previous work [4] will serve as a basis for the later comparison of results, we would like to clarify the differences between that work and this one in terms of approaches and goals.