## 4 Discussion
### Discussion Related to Other Works  
Image conversion task is an active research topic, in particular for coloring black and white images as well as adding styles to images (style transfer). These tasks are solved using various techniques, in particular, methods that use machine learning are effective. Among the methods of machine learning, the most spectacular results are obtained with the use of deep learning convolutional neural networks [47]. There are basically two goals for this type of transformation: to achieve consistency at the microstructure level and to have a good visual subjective transformation from the human point of view. Subjective acceptance for humans is important in, for example, artistic applications, where the mapping does not have to be repetitive, but is even allowed to introduce innovations in the generated image using a noise generator. These types of algorithms do not meet the requirements for medical or biological applications, where achieving microstructure compliance is a key property.  
The main problem of image generating algorithms with the condition of preserving the microstructure is their complexity and sensitivity to new data that did not occur during the training. This means that it is necessary to obtain a good approximator with a good spatial location of the generated features (for example edges). With ConvNN, localization problems come from the use of pooling layers. Poolinglayers are used in segmentation algorithms and are typical of most ConvNN architectures. For this reason, such layers were rejected in the proposed network, since a unidirectional multi-layer network can map the microstructures.  
The problem of loss of spatial resolution and localization of microstructures is known and there are other approaches such as U-Net [48]. In the U-Net network, the encoder-decoder architecture is used, but the decoder uses the images in different scales from the encoder to synthesize it. This allows for image segmentation while preserving the microstructure. The GAN framework was proposed in [15], where the generator is the U-Net. This shows that different approaches are possible, but a comparative evaluation of different solutions is difficult. The comparison of different network structures requires a Monte Carlo study to determine unbiased estimators of image conversion quality. This requires many learning processes for each network (minimum 10, but hundreds and thousands are also possible). As networks can have different architectures: the number of layers, the number of neurons, the sizes of convolution masks, etc., the number of analyzed cases is huge. This process can be paralleled, but very high computing power is necessary. Unfortunately, the results depend on the learning base, which in the case of medical images means that the result of the search for optimal solutions is specific and depends on the type of images (tissues, enlargements, etc.). Inference about the best architecture choice from a case-by-case comparison usually leads to wrong conclusions.  
Another option to improve the quality or optimize the conversion system is to use the radiomics method [49; 50]. This method extracts a large number of features from radiographic medical images using data characterization algorithms. The obtained features may be inputs to ConvNN, so these or similar features required not to be learned by the network if they are well matched. The use of the radiomics method is quite difficult for ConvNN. In the case of an RGB image, there are only three image channels (R, G, B), and PCA uses one channel, while for radiomics, there may be hundreds of channels. This means that there are many more weights to learn. The amount of available GPU memory for calculations is also a serious problem. It is possible that the use of feature optimization may be an interesting solution, but it is an expensive process, because the network requires being repeatedly trained in order to determine the effectiveness of the feature set.