Machine Learning-Based Privacy Preserving via CT/MRI and Organ Metadata Prediction

Medical imaging plays a significant role in diagnosis and treatment planning, with significant efforts focused on training machine learning (ML) algorithms to perform detection and classification automatically. However, concerns regarding patient privacy have prompted the need for robust deidentification of the data. In this paper, we consider a general scenario in which the data are entirely anonymized by removing the metadata due to privacy concerns. In several circumstances, efficient training of an arbitrary downstream ML model requires some prior information that is only accessible through metadata. To address this need, we propose a novel approach for automated metadata prediction from fully anonymized CT and MRI images to enable efficient training of the downstream model. Our method accurately identifies the imaging modality (CT or MRI) and anatomical region (heart, brain, or liver) directly from image data. Moreover, in case of MRI data, it allows for contrast classification into T1 and T2. Our framework employs a set of machine learning as well as deterministic techniques to perform these tasks, achieving high accuracy rates in distinguishing between CT and MRI scans and localizing anatomical regions, as well as classification of MRI imaging protocol, T1 and T2. By estimating technical metadata from anonymized data, our approach successfully combines multiple tasks into a unified framework designed for privacy-preserving workflows. We demonstrate the effectiveness and practicality of our method through systematic experiments on medical imaging datasets. On held-out test sets, the framework achieved 100% accuracy for CT/MRI modality detection; 99.2% accuracy for anatomical region classification across brain, heart, and liver volumes (125/126 correctly classified); and 99.8% accuracy for MRI T1/T2 protocol classification (500/501 correctly classified). The proposed framework represents a new direction toward full privacy protection in medical imaging with no impact on the selection of appropriate image processing frameworks in the downstream tasks.