Subedi, Ronast;
Gaire, Rebati Raman;
Ali, Sharib;
Nguyen, Anh;
Stoyanov, Danail;
Bhattarai, Binod;
(2023)
A Client-Server Deep Federated Learning for Cross-Domain Surgical Image Segmentation.
In: Bhattarai, Binod and Ali, Sharib and Rau, Anita and Nguyen, Anh and Namburete, Ana and Caramalau, Razvan and Stoyanov, Danail, (eds.)
Data Engineering in Medical Imaging: DEMI 2023.
(pp. pp. 21-33).
Springer: Cham, Switzerland.
Preview |
Text
A Client-Server Deep Federated Learning for Cross-Domain Surgical Image Segmentation.pdf - Published Version Download (3MB) | Preview |
Abstract
This paper presents a solution to the cross-domain adaptation problem for 2D surgical image segmentation, explicitly considering the privacy protection of distributed datasets belonging to different centers. Deep learning architectures in medical image analysis necessitate extensive training data for better generalization. However, obtaining sufficient diagnostic and surgical data is still challenging, mainly due to the inherent cost of data curation and the need of experts for data annotation. Moreover, increased privacy and legal compliance concerns can make data sharing across clinical sites or regions difficult. Another ubiquitous challenge the medical datasets face is inevitable domain shifts among the collected data at the different centers. To this end, we propose a Client-server deep federated architecture for cross-domain adaptation. A server hosts a set of immutable parameters common to both the source and target domains. The clients consist of the respective domain-specific parameters and make requests to the server while learning their parameters and inferencing. We evaluate our framework in two benchmark datasets, demonstrating applicability in computer-assisted interventions for endoscopic polyp segmentation and diagnostic skin lesion detection and analysis. Our extensive quantitative and qualitative experiments demonstrate the superiority of the proposed method compared to competitive baseline and state-of-the-art methods. We will make the code available upon the paper’s acceptance. Codes are available at: https://github.com/bhattarailab/federated-da.
| Type: | Proceedings paper |
|---|---|
| Title: | A Client-Server Deep Federated Learning for Cross-Domain Surgical Image Segmentation |
| Event: | MICCAI Workshop on Data Engineering in Medical Imaging: DEMI 2023 |
| ISBN-13: | 978-3-031-44991-8 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-44992-5_3 |
| Publisher version: | http://dx.doi.org/10.1007/978-3-031-44992-5_3 |
| 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: | Domain Adaptation; Federated Learning; Decentralised Storage; Privacy |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10185001 |
Archive Staff Only
 |
View Item |
