# Unsupervised Cyclic Siamese Networks Automating Cell Imagery Analysis
## 3 Methodology
### Architecture and Learning Scheme
#### 3.3.3 Baselines  
For the evaluation in the upcoming section, several baselines have been gathered, to enable a meaningful comparison with the state of the art. The first baseline is a widely practiced classical computer vision pipeline. First, the input images are cropped to only contain the cell chamber, and are then blurred with an averaging kernel-based filter; then, a thresholding filter is applied, followed by a watershed segmentation [29]. The regions of the segmented image are counted and used as a cell estimation. In order to find suitable parameters for this learning scheme, an exhaustive grid search was performed for each data set BF and PC. The code repository contains the best hyperparameters found. We refer to this pipeline as Watershed in the following. As a second baseline, we fine-tuned a pre-trained state of the art deep convolution neural network, specifically a variant of EfficientNet [28]. We replace the last layer of the pre-trained network with a fully connected layer that outputs a single value, and train it to predict the cell count for a given input image. We apply the same hyperparameter optimization as for our own method, and generate the same data augmentation. Since EfficientNet is a variable architecture that comes in different sizes, referred to as EfficientNet-B0, EfficientNet-B1, and so on, we evaluated EfficientNet-B0 through EfficientNet-B3 and found that the smallest variant EfficientNet-B0 performed best, while larger variants performed progressively worse. We considered to instead use EfficientNetV2 [47], but our preliminary results showed that the same performance degradation applies to its larger variants as well, and since EfficientNet-B0 outperformed the smallest EfficientNetV2-S variant, we retained it and refer to this fine-tuned convolutional neural network as EfficientNet hereafter. As a third baseline, we compare a state of the art transfer learning model from [PERSON] et al. called BiT, which produces highly accurate classification results on Cifar-100 and similar data sets in a few-shot learning case of 1 to 10 examples per class. BiT consists of the classical ResNet [48] architecture, but with very long pre-training times on large image sets and a custom hyperrule that determines the training time and learning rate during transfer depending on the size of the new data set. Changes to the hyperrule were tested, but did not cause any significant improvement in accuracy; therefore, the values provided by the authors were used. BiT is given all the natural and synthetic training data per epoch, so it can come up with meaningful cell counts on natural data by abstracting from the labeled synthetic data. We valued the possible cell counts from 1 to 30 as classes, to account for the difference in training methodology. In addition, we compare our own previous work Twin-VAE (see [3]) and its alterations Transfer Twin-VAE and Dual Transfer Twin-VAE (see [4]). These are based on the same architecture, but perform semi-supervised learning techniques for which the same data are used, albeit with partial annotations on the natural training data of 5-10%. Although this circumstance should allow for higher accuracy on the counting task, the optimized pipeline and cyclic data reuse of the new Siamese-Cycle-VAE is able to keep up with and in some cases even outperform its predecessors, despite not being given any manual labels at all. More on this is given below. Lastly, ablation studies are done to ensure and show that the specific architectural details and principles of the learning scheme are helpful and optimize the training procedure and therefore the accuracy on the regression task. One study will be called C-VAE from here on. In this alteration of the network, there are no specialized Siamese encoders and decoders, but the cyclic structure is kept. C-VAE should still be able to make meaningful cell predictions, albeit that the abstraction between natural and synthetic data has to happen in the inner layers of the VAE.