# Adaptation to CT Reconstruction Kernels by Enforcing Cross-Domain Feature Maps Consistency
### Related Work  
We begin with discussing the task-specific augmentation approaches since they are a straightforward solution to the domain shift problem. Contrary to the classical augmentation techniques for CT images like windowing [17] or filtering [18], the authors of [14] the proposed method called FBPAug. Notably, the latter augmentation technique directly approximates our domain shift. The authors also showed that FBPAug outperforms other augmentations. Thus, we consider FBPAug as one of the solutions and compare it with the other methods.  
With the unlabeled target data, we can apply unsupervised domain adaptation methods to improve the model's performance on the target domain. The adversarial approaches are shown to outperform other methods [15]. Considering the paired nature of our data, we divide the adversarial methods into two groups: (i) image-to-image translation and (ii) feature-level adaptation.  
The first group of methods aims to translate an image from source to target domain. In [19], the authors used CycleGAN for unpaired image-to-image translation. Further, this method was implemented both for the MRI [20] and CT [21] image translations. The paired image-to-image translation is closer to our setup. Such a method requires image pairs that have a different style but the same semantic content. CT images that differ only in the reconstruction kernel correspond to this setup. Here, the authors of [13] and [12] proposed a convolutional neural network (CNN) to translate the images reconstructed with one kernel to another kernel. However, we need to train a separate model for every pair of kernels. Our setup includes seven known kernels that already yield training of 42 translation models. The number of models grows in quadratic progression depending on the number of kernels. Therefore, the image translation methods lack the generalization ability in our setup and we do not consider them.  
The feature-level adaptation methods are independent of the number of domains. Mostly, these methods are based on adversarial learning as in [15]. The latter approach also finds several successful applications in medical imaging, e.g., [22; 23]. As these methods are conceptually close to each other, we stick with implementing a domain adversarial neural network (DANN) from [15].  
The idea of F-Consistency is conceptually close to self-supervised learning, where the unlabeled data is used to train a model using pseudo-labels. In [24], the authors described different pretext tasks in medical imaging. Similar to our approach, the authors [25] enforce the model consistency for the initial and augmented images at the prediction and feature levels. Furthermore, the authors [26] extended the self-supervised methodology to solve a domain adaptation problem. However, the goal of the self-supervised methods is to use a large collection of images without annotations to improve the model's performance on the source domain. Contrary to self-supervised learning, our goal is to achieve the highest possible performance on the target domain.  
Finally, we note that there is no standardized benchmark for the COVID-19 segmentation task [27]. However, we also note that most of the COVID-19 segmentation approaches use the same U-Net-based model; thus, our method could be generalized to all these setups. Moreover, our domain adaptation method could be translated from segmentation to classification or detection tasks.