Abstract
Chest CT scans are essential in diagnosing lung abnormalities, including lung cancer, but their utility in training deep learning models is often pushed back by limited data availability, high labeling costs, and privacy concerns. To address these challenges, this study explores the use of score-based diffusion models for the conditional generation of lung CT scans slices. Two generation scenarios are explored: one limited to lung segmentation masks and another incorporating both lung and nodule segmentation mappings to guide the synthesis process. The proposed methods are custom U-Net architecture models trained to predict the scores in Variance Preserving (VP) and Variance Exploding (VE) Stochastic Differential Equations (SDEs), composing the primary ground for comparison in conditional sample generation. The results demonstrate the VP SDEs model's superiority in generating high-fidelity images, as evidenced by high SSIM (0.894) and PSNR (28.6) values, as well as low domain-specific FID (173.4), MMD (0.0133) and ECS (0.78) scores. The generated images consistently followed the conditional mapping guidance during the generation process, effectively producing realistic lung and nodule structures, highlighting their potential for data augmentation in medical imaging tasks. While the models achieved notable success in generating accurate 2D lung CT scan slices given simple conditional image region mappings, future work surrounds the extension of these methods to 3D conditional generation and the use of richer conditional mappings to account for broader anatomical variations. Nevertheless, this study holds promise for improvement in computer-aided systems through the support in deep learning model training for lung disease diagnosis and classification.

Abstract
Cancer is a leading cause of mortality worldwide, with breast and lung cancer being the most prevalent globally. Early and accurate diagnosis is crucial for successful treatment, and medical imaging techniques play a pivotal role in achieving this. This paper proposes a novel pipeline that leverages generative artificial intelligence to enhance medical images by combining synthetic image generation and super-resolution techniques. The framework is validated in two medical use cases (breast and lung cancers), demonstrating its potential to improve the quality and quantity of medical imaging data, ultimately contributing to more precise and effective cancer diagnosis and treatment. Overall, although some limitations do exist, this paper achieved satisfactory results for an image size which is conductive to specialist analysis, and further expands upon this field's capabilities.

Abstract
Advancements in the care for patients with breast cancer have demanded the development of biomechanical breast models for the planning and risk mitigation of such invasive surgical procedures. However, these approaches require large amounts of high-quality magnetic resonance imaging (MRI) training data that is of difficult acquisition and availability. Although this can be solved using synthetic data, generating high resolution images comes at the price of very high computational constraints and tipically low performances. On the other hand, producing lower resolution samples yields better results and efficiency but falls short of meeting health professional standards. Therefore, this work aims to validate a joint approach between lower resolution generative models and the proposed super-resolution architecture, titled Shifted Window Image Restoration (SWinIR), which was used to achieve a 4x increase in image size of breast cancer patient MRI samples. Results prove to be promising and to further expand upon the super-resolution state-of-the-art, achieving good maximum peak signal-to-noise ratio of 41.36 and structural similarity index values of 0.962 and thus beating traditional methods and other machine learning architectures.