Federated Voxel Scene Graph for Intracranial Hemorrhage
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915001716965376 |
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| author | Sanner, Antoine P. Stieber, Jonathan Grauhan, Nils F. Kim, Suam Brockmann, Marc A. Othman, Ahmed E. Mukhopadhyay, Anirban |
| author_facet | Sanner, Antoine P. Stieber, Jonathan Grauhan, Nils F. Kim, Suam Brockmann, Marc A. Othman, Ahmed E. Mukhopadhyay, Anirban |
| contents | Intracranial Hemorrhage is a potentially lethal condition whose manifestation is vastly diverse and shifts across clinical centers worldwide. Deep-learning-based solutions are starting to model complex relations between brain structures, but still struggle to generalize. While gathering more diverse data is the most natural approach, privacy regulations often limit the sharing of medical data. We propose the first application of Federated Scene Graph Generation. We show that our models can leverage the increased training data diversity. For Scene Graph Generation, they can recall up to 20% more clinically relevant relations across datasets compared to models trained on a single centralized dataset. Learning structured data representation in a federated setting can open the way to the development of new methods that can leverage this finer information to regularize across clients more effectively. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_00578 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Federated Voxel Scene Graph for Intracranial Hemorrhage Sanner, Antoine P. Stieber, Jonathan Grauhan, Nils F. Kim, Suam Brockmann, Marc A. Othman, Ahmed E. Mukhopadhyay, Anirban Computer Vision and Pattern Recognition Distributed, Parallel, and Cluster Computing Image and Video Processing 68T07 I.2.10 Intracranial Hemorrhage is a potentially lethal condition whose manifestation is vastly diverse and shifts across clinical centers worldwide. Deep-learning-based solutions are starting to model complex relations between brain structures, but still struggle to generalize. While gathering more diverse data is the most natural approach, privacy regulations often limit the sharing of medical data. We propose the first application of Federated Scene Graph Generation. We show that our models can leverage the increased training data diversity. For Scene Graph Generation, they can recall up to 20% more clinically relevant relations across datasets compared to models trained on a single centralized dataset. Learning structured data representation in a federated setting can open the way to the development of new methods that can leverage this finer information to regularize across clients more effectively. |
| title | Federated Voxel Scene Graph for Intracranial Hemorrhage |
| topic | Computer Vision and Pattern Recognition Distributed, Parallel, and Cluster Computing Image and Video Processing 68T07 I.2.10 |
| url | https://arxiv.org/abs/2411.00578 |