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Lung cancer remains one of the most common and lethal forms of cancer, with approximately 1.8 million deaths annually, often diagnosed at advanced stages. Early detection is crucial, but it depends on physicians' accurate interpretation of computed tomography (CT) scans, a process susceptible to human limitations and variability. ByMe has developed a medical image annotation and anonymization tool designed to address these challenges through a human-centered approach. The tool enables physicians to seamlessly add structured attribute-based annotations (e.g., size, location, morphology) directly within their established workflows, ensuring intuitive interaction.Integrated with Picture Archiving and Communication Systems (PACS), the tool streamlines the annotation process and enhances usability by offering a dedicated worklist for retrospective and prospective case analysis. Robust anonymization features ensure compliance with privacy regulations such as the General Data Protection Regulation (GDPR), enabling secure dataset sharing for research and developing artificial intelligence (AI) models. Designed to empower AI integration, the tool not only facilitates the creation of high-quality datasets but also lays the foundation for incorporating AI-driven insights directly into clinical workflows. Focusing on usability, workflow integration, and privacy, this innovation bridges the gap between precision medicine and advanced technology. By providing the means to develop and train AI models for lung cancer detection, it holds the potential to significantly accelerate diagnosis as well as enhance its accuracy and consistency.

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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 superresolution 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. © 2025 IEEE.

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. © 2025 Elsevier B.V., All rights reserved.