# Federated Voxel Scene Graph for Intracranial Hemorrhage

arXiv: https://arxiv.org/abs/2411.00578 · HTML: https://mlsanity.com/paper/2411.00578/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Builds on (most cited first, 13 of 13)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Scene Graph Generation by Iterative Message Passing](https://mlsanity.com/paper/1701.02426/) (2017)
- [Neural Motifs: Scene Graph Parsing with Global Context](https://mlsanity.com/paper/1711.06640/) (2017)
- [Unbiased Scene Graph Generation from Biased Training](https://mlsanity.com/paper/2002.11949/) (2020)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection](https://mlsanity.com/paper/1811.08661/) (2018)
- [BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset](https://mlsanity.com/paper/2308.11298/) (2023)
- [The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge](https://mlsanity.com/paper/2301.03281/) (2023)
- [Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans](https://mlsanity.com/paper/1803.05854/) (2018)
- [Voxel Scene Graph for Intracranial Hemorrhage](https://mlsanity.com/paper/2407.21580/) (2024)
- [Weakly Supervised Intracranial Hemorrhage Segmentation with YOLO and an Uncertainty Rectified Segment Anything Model](https://mlsanity.com/paper/2407.20461/) (2024)
- [Detection of Intracranial Hemorrhage for Trauma Patients](https://mlsanity.com/paper/2408.10768/) (2024)

## Abstract (arXiv)

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.