# The Holistic Perspective of the INCISIVE Project--Artificial Intelligence in Screening Mammography
## 1 Introduction  
Cancer offers a unique context for medical decisions, given not only due to its variegated forms with the evolution of the disease, but also regarding the need to consider the individual condition of patients, their ability to receive treatment, and their responses to treatment [1]. Despite improved technologies, challenges remain in the accurate and early detection, tumor classification/characterization, the prediction of tumor evolution (either locally, recurrently, or metastatically), and the precise evaluation of treatment schemas and follow-up monitoring of cancer. Medical imaging is an important part of cancer protocols applied mainly (but not limited) to diagnosis and detection stages, providing a variety of information on thetumor's exact location, structure, metabolism, and functions [2]. The main advantages of medical imaging techniques include monitoring capability in real time, minimally invasive procedures, early detection (even for asymptomatic patients), accurate information on the tumor's precise location, as well as details on its size and morphology. Most importantly, they support healthcare providers in the definition of the treatment plan, the evaluation of its effectiveness, as well as follow-up interventions. The increasing amount and availability of collected data (including cancer imaging) and the development of novel technological tools based on artificial intelligence (AI) and machine learning (ML) provide unprecedented opportunities for improved cancer detection and classification [3], tumor segmentation [4], image optimization, radiation reduction [5], and clinical workflow enhancement. The current imaging methods may be enhanced by identifying findings, whether detectable or not by the human eye, moving from a subjective perceptual skill to a more objective one. So far, on the one hand, an already-huge amount of cancer data, including imaging data, increases every day. However, the datasets are scattered all around the world and are not used efficiently. On the other hand, AI and machine learning techniques that can provide efficient cancer disease management pathways already exist. However, there is a lack of publicly available AI-ready imaging data, and there is poor confidence in AI-based technology among healthcare professionals for disease diagnosis, prediction, and follow-up [6]. In order to address the challenges related to AI in health imaging, the European commission had issued a tailored call and funded four research and innovation actions in 2020. INCISIVE (a multimodal AI-based toolbox and an interoperable health imaging repository for the empowerment of imaging analysis related to the diagnosis, prediction, and follow-up of cancer, no 952179), as one of them, brings together 27 partners from 9 countries (Belgium, Cyprus, Finland, Greece, Italy, Luxemburg, Serbia, Spain, and the UK), aiming to enhance cancer diagnosis and prediction using AI and Big Data in four cancer cases: breast, lung, colorectal, and prostate cancer. In the context of the INCISIVE project, a wide variety of existing cancer images, e.g., computerized tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), ultrasounds (USs), \(X\)-rays, and mammograms (MGs), are exploited and further combined with other available data sources, such as biological, medical history, and clinical analysis, offering a critical mass of available data in the envisaged repository of medical images, enabling the excessive training of foreseen AI models. The final outcome was envisioned as a platform offering an AI-based toolbox and an interoperable federated pan-European data repository with secure data sharing and data donorship schema, while at the same time being engineered to meet user needs and improve user acceptance.