A Vendor Neutral Archive with MONAI for Automatic Medical Image Analysis

Computer-assisted diagnosis has been advancing significantly with the proliferation of intelligent image analysis algorithms. These tools enhance medical image screening and diagnostic workflow by highlighting relevant features for doctors, resulting in faster and more accurate diagnoses. However, developing these tools requires large annotated image datasets for achieving acceptable performance. Moreover, developing the analysis algorithms requires knowledge about these technologies, often limited to medical staff. Finally, the integration of those solutions in clinical environments is still a hard barrier due to interoperability issues. This work proposes a solution that extends a Vendor-Neutral Archive for supporting the integration of AI tools for research and production. It makes use of two open-source components, Dicoogle and MONAI, resulting in aWeb and DICOM compliant platform that uses active learning strategies for automatic image annotation and inference services.