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.
Original language | English |
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Title of host publication | Data Engineering in Medical Imaging |
Subtitle of host publication | First MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings |
Editors | Binod Bhattarai, Sharib Ali, Anita Rau, Anh Nguyen, Ana Namburete, Razvan Caramalau, Danail Stoyanov |
Place of Publication | Cham |
Publisher | Springer |
Pages | 21-33 |
Number of pages | 13 |
ISBN (Electronic) | 978-3-031-44992-5 |
ISBN (Print) | 978-3-031-44991-8 |
DOIs | |
Publication status | Published - 1 Oct 2023 |
Event | 1st MICCAI Workshop on Data Engineering in Medical Imaging, DEMI 2023 - Vancouver, Canada Duration: 8 Oct 2023 → 8 Oct 2023 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 14314 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 1st MICCAI Workshop on Data Engineering in Medical Imaging, DEMI 2023 |
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Country/Territory | Canada |
City | Vancouver |
Period | 8/10/23 → 8/10/23 |
Bibliographical note
Data Engineering in Medical Imaging - 1st MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, ProceedingsFunding Information:
This work is partly supported by the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) [203145Z/16/Z]; Engineering and Physical Sciences Research Council (EPSRC) [EP/P027938/1, EP/R004080/1, EP/P012841/1]; The Royal Academy of Engineering Chair in Emerging Technologies scheme; and the EndoMapper project by Horizon 2020 FET (GA 863146).
Keywords
- Decentralised Storage
- Domain Adaptation
- Federated Learning
- Privacy