Sanner, A. P., Stieber, J., Grauhan, N. F., Kim, S., Brockmann, M. A., Othman, A. E., & Mukhopadhyay, A. (2024). Federated Voxel Scene Graph for Intracranial Hemorrhage. arXiv preprint arXiv:2411.00578.
This research paper introduces a novel approach called Federated Voxel Scene Graph Generation (Fed-V-SGG) to improve the detection and analysis of intracranial hemorrhage (ICH) from head CT scans. The authors aim to address the limitations of traditional deep learning models trained on centralized datasets, which often struggle to generalize to diverse ICH manifestations and data distributions encountered in real-world clinical settings.
The researchers propose two Fed-V-SGG methods, Fed-MOTIF and Fed-IMP, which leverage federated learning principles to train models collaboratively across multiple datasets without sharing patient data. These methods employ a 3D Retina-UNet architecture for object detection (bleeding, ventricle system, midline) and incorporate Neural Motifs (MOTIF) or Iterative Message Passing (IMP) techniques for relation prediction between detected objects. The models are trained and evaluated on four datasets from different clinical centers worldwide: INSTANCE2022, BHSD, CQ500, and a private cohort from Germany.
This study highlights the potential of federated learning in developing more accurate and generalizable deep learning models for medical image analysis, particularly in the context of ICH detection and analysis. Fed-V-SGG offers a promising solution for overcoming data privacy concerns and leveraging the wealth of information distributed across multiple clinical centers to improve patient care.
This research significantly contributes to the field of medical image analysis by introducing a novel application of federated learning for ICH detection and analysis using voxel scene graphs. The proposed Fed-V-SGG methods have the potential to enhance clinical decision-making, facilitate early diagnosis, and improve treatment outcomes for patients with ICH.
The study is limited by the relatively small size of the private cohort dataset. Future research could explore the application of Fed-V-SGG to larger and more diverse datasets to further validate its effectiveness and generalizability. Additionally, investigating the integration of other clinical variables and modalities, such as patient demographics and MRI scans, could further enhance the models' predictive capabilities.
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by Antoine P. S... at arxiv.org 11-04-2024
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