# Unsupervised Cyclic Siamese Networks Automating Cell Imagery Analysis
## 1 Introduction
The novelty of [4] is state of the art accuracy in the domain of semi-supervised cell counting, achieved by transferring a pre-trained model to another type of microscopy data. Due to optimizations in the transfer process, the architecture presented there has also slightly outperformed the previous state of the art. In this work, we instead focus on unsupervised training with the modification of generating pseudo-synthetic images from natural images (and vice versa) in order to use the well-trained regressor that is accustomed to synthetic data representations. The earlier work would not be able to achieve meaningful regression for the fully unlabeled natural data used in this work because the loss of the regressor would not be defined for natural data. Figure 1 shows examples from the MSCC experiments that we address in the following. It can be seen that lab-on-a-chip technology is used and that the data have a number of visual aspects that make them difficult for classical image processing solutions and non-specialized machine learning models to process. Namely, these are as follows:  
* Smudges, in some cases larger than cells. Simple background filtering does not work, as these can move during the experiment. * Ongoing cell divisions (Figure 1 right), making it unclear in some cases what the actual correct target variable would be, but giving a meaning to comma values as they can represent an ongoing division. * Varying contrast and light conditions. * Dying, appearance, and vanishing of cells. * Overpopulation of the cell chamber or the end of an experiment due to escape of the cells. * Overlapping and close adherence of cells. * Continuous changes in the cell membrane and inner organelles, changing the orientation of cells, with variations in shape and perceived size. In this article, we propose a novel training scheme for a Siamese deep learning model that can optimally combine information provided by automatically generated synthetic data and real images such that no manual labeling of natural data is required. The contribution and novelty of this work are as follows:  
* We achieve high prediction preciseness on the target variable where the state of the art fails to do so. * We build an effective translation learning pipeline and show, on multiple microscopy data sets, that this pipeline is stable and reliable throughout this domain. * We gain additional insight into the inner state of the neural network by performing translations twice (cycling), leading to critical parts of the architecture to optimize the network for the domain without overfitting to the specific data, thus contributing to the understanding of deep neural network representations, especially for Siamese networks [6]. In the following sections, we first give an overview of the current state of the art in this research field and take a brief look at previous works in this field of application. In Section 3, we address the underlying machine learning challenge and present our deep Siamese network architecture in detail. Then, the details of the proposed learning procedure are explained and it is analyzed how the unique architecture used affects the learning procedure. Thereafter, Section 4 contains the evaluation for real data sets and ablation studies, as well as the comparison to state of the art alternatives and baselines. Lastly, in Section 5, a discussion followed by a conclusion (see Section 6) completes the contribution.