Smart-Uploader: an automatic tool for medical image classification and quality protocol adherence

Motivation: Imaging biomarkers are becoming a cornerstone to increase throughput and efficiency of large clinical trials. However, the diversity of imaging modalities used to derive them creates complexity for both imaging protocols and image archiving systems. Goal(s): To develop a tool to automatically classify imaging modalities and assess their adherence to the predefined acquisition protocol. Approach: The combination of a few-shot learning classifier trained to classify image modalities according to their contrast characteristics and a deterministic heuristic approach based on the DICOM headers. Results: The proposed approach displays potential for automatic online image classification and identification of protocol deviations, increasing clinical trial operational efficiency. Impact: The proposed joint approach automatically classifies all medical imaging data and assesses its adherence to the predefined acquisition protocol. Consequently, it not only facilitates data management but also identifies protocol deviations increasing the operational efficiency of clinical trials.