Galati, Francesco;
Cortese, Rosa;
Prados, Ferran;
Lorenzi, Marco;
Zuluaga, Maria A;
(2024)
Federated Multi-centric Image Segmentation with Uneven Label Distribution.
In: Linguraru, MG and Dou, Q and Feragen, A and Giannarou, S and Glocker, B and Lekadir, K and Schnabel, JA, (eds.)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2024.
(pp. pp. 350-360).
Springer: Cham, Switzerland.
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Abstract
While federated learning is the state-of-the-art methodology for collaborative learning, its adoption for training segmentation models often relies on the assumption of uniform label distributions across participants, and is generally sensitive to the large variability of multi-centric imaging data. To overcome these issues, we propose a novel federated image segmentation approach adapted to complex non-iid setting typical of real-life conditions. We assume that labeled dataset is not available to all clients, and that clients data exhibit differences in distribution due to three factors: different scanners, imaging modalities and imaged organs. Our proposed framework collaboratively builds a multimodal data factory that embeds a shared, disentangled latent representation across participants. In a second asynchronous stage, this setup enables local domain adaptation without exchanging raw data or annotations, facilitating target segmentation. We evaluate our method across three distinct scenarios, including multi-scanner cardiac magnetic resonance segmentation, multi-modality skull stripping, and multi-organ vascular segmentation. The results obtained demonstrate the quality and robustness of our approach as compared to the state-of-the-art methods.
| Type: | Proceedings paper |
|---|---|
| Title: | Federated Multi-centric Image Segmentation with Uneven Label Distribution |
| Event: | 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) |
| Location: | MOROCCO, Palmeraie Conf Ctr, Marrakesh |
| Dates: | 6 Oct 2024 - 10 Oct 2024 |
| ISBN-13: | 978-3-031-72116-8 |
| DOI: | 10.1007/978-3-031-72117-5_33 |
| Publisher version: | https://doi.org/10.1007/978-3-031-72117-5_33 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Computer Science, Computer Science, Artificial Intelligence, Computer Science, Theory & Methods, Domain Adaptation, Federated Learning, Image Segmentation, Life Sciences & Biomedicine, Missing Labels, Radiology, Nuclear Medicine & Medical Imaging, Science & Technology, Technology |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10203452 |
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