# The Holistic Perspective of the INCISIVE Project--Artificial Intelligence in Screening Mammography
## 4 Results to the Existing Data Repository
### Data Sharing
However, single research centers cannot produce enough data to fit prognostic and predictive models of sufficient accuracy, resulting in algorithms with limited diversity and generalizability to widespread clinical practice. Data sharing in cancer imaging is therefore of utmost importance. Standards, including the PACS and the DICOM, ensure standardized imaging data structure and communication/retrieval. However, most of the imaging datasets are often heavily protected and not accessible for research purposes or accessible imaging data, and are often unusable because they are not appropriately curated, organized, de-identified, or annotated. The imaging data curationinvolves trained professionals, making the process time- and labor-intensive. Moreover, the training of deep networks requires balanced sample datasets as the inequality of training data distribution can induce biased models. Limited data-sharing initiatives exist, such as the Cancer Imaging Archive (TCIA) and the National Institutes of Health databases among others, whereas the extent of data sharing required for the widespread adoption of AI-based technologies across health systems will require more expansive efforts. Consequently, few appropriate image datasets are available for the research and vendor community to support the development of automated clinical solutions and the limited training data act as a bottleneck for the further application of deep learning (DL) methods in medical image analysis. To address these gaps, large-scale imaging datasets with standardized validation sets that capture a comprehensive diversity of test images (including both routine and challenging cases) will need to be developed for each application in combination with more effective auto- or semi-automated methods for data management standardization to support both clinical practice and research. One of the major goals of INCISIVE is to address the data availability challenge towards the wide adoption of AI solutions in health imaging. INCISIVE will aggregate and unify the fragmented cancer imaging datasets across European healthcare systems and institutions, characterized by a multiplicity of data sources, enabling the integration and full exploitation of current initiatives and isolated databases to reach a critical mass of gathered data. INCISIVE proposes and implements the pan-European repository of health images following a federated approach, aiming to break the silos by incorporating mechanisms for secure data sharing and ensuring that the data providers keep full control of their data and their right of opting out. The rationale behind this decentralized approach is to minimize centralized storage operations, bringing the INCISIVE features to the local level. Blockchain technology is utilized to achieve transparency and traceability. A data donorship mechanism is incorporated to empower both patients and institutions to contribute imaging data for responsible research, allowing data sources to be easily linked to the system and human quality ratings to be acquired. Compliance to the corresponding legal directives is embodied to the core of the repository, while corresponding standards (HL7 FHIR, DICOM, and SNOMED) are utilized to enhance interoperability and connectivity. Above all, INCISIVE ML-aided data curation, de-identification, and annotation reduce efforts and human interventions relating to these tasks, thus improving the accessibility to isolated datasets even further, making them available for research purposes. The use of data during and beyond INCISIVE is based on FAIR principles to make data discoverable and processable both for human- and machine-driven activities. This will increase their interoperability with current and future imaging cancer datasets, allowing researchers to design and develop AI tools beyond the project's current scope. The INCISIVE pan-European repository implementation is based on two major axes.