## 2 The Papers
### Image Generation  
Within the domain of medical imaging, generative models pursue two primary trajectories: (i) transformation from noise to image and (ii) transition between images. The former encompasses methodologies focused on artificially augmenting the dataset, often referred to as augmentation, by training a deep learning-grounded model to transmute a noise vector into authentic-looking images. To illustrate, when confronted with an extensive array of breast imaging data, such as mammograms, a generative model endeavors to fabricate novel images resembling constituents of the imaging dataset. In the context of image-to-image generation, generative models undertake the conversion of a given input image into an alternative representation. A diverse array of challenges within the healthcare domain can be effectively addressed through image-to-image generative models. These encompass tasks spanning the enhancement of image fidelity via denoising, amplification of resolution, image inpainting, amalgamation of multi-modal images, along with image reconstruction and alignment. In the field of reconstruction and image generation four papers are presented. The first one suggests employing an end-to-end deep learning network for the correction of metal artifacts in CT images [6]. The input of the network is represented by metal-affected NMAR sinograms, and the outputs are artifact-free reconstructed images. The architecture consists of three parts: sinogram refinement used to filter the sinogram, back projection used to reconstruct the image into the image domain, and image refinement used to further refine the reconstruction. All parts are trained simultaneously and furthermore, the network performs the complete CT image reconstruction, and does not require a predefined back projection operator or the exact X-ray beam geometry. A second paper in this area proposes a method to increase the field of view of intraoperative images obtained from Computer Tomographs [7]. This method is used as a prior step to the registration of two volumes: thin intraoperative volume and preoperative volume. The method consists in extrapolating the thin volume by generating additional slices from the existing ones using a GAN architecture. By enhancing the context information required for the matching process, the results appear to be comparable to those obtained after aligning two high-resolution images having the same field of view. The third paper presents a transfer learning enhanced GAN technique for image reconstruction using under-sampled MR data [8]. The model was tested on an open-source knee dataset, and a private brain dataset with two different acceleration factors: 2 and 4. Both datasets were divided into training and test sets. The training sets were used for finetuning the model after transfer learning. The results indicate that the proposed model outperforms the other reconstruction techniques for both acceleration factors, suggesting that, by using transfer learning, the variation in image contrast, acceleration factor and anatomy between training and test dataset is smaller. Moreover, the distribution of the reconstructed images, produced by transfer learning is more similar to the distribution of the completely sampled image. The main objective of the fourth paper is OCT image enhancement through denoising and deblurring of the image on a single step process [9]. The applied method is an unsupervised learning technique with unpaired images and disentangled representation, combined with a GAN architecture. The framework consists in encoders (used to extract relevant features from the raw images: image content, image noise, blur features, and blur-noise features), generators (used to generate from the extracted features blurred, noisy, blurred-noisy and clean images) and discriminators (used to discriminate between generated and real images for each feature). The obtained model was compared with state-of-the-art methods for OCT image enhancement, which were outperformed.