Xu, Jialang;
Wang, Jiacheng;
Yu, Lequan;
Stoyanov, Danail;
Jin, Yueming;
Mazomenos, Evangelos B;
(2025)
Personalizing Federated Instrument Segmentation With Visual Trait Priors in Robotic Surgery.
IEEE Transactions on Biomedical Engineering
10.1109/tbme.2025.3526667.
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Abstract
Personalized federated learning (PFL) for surgical instrument segmentation (SIS) is a promising approach. It enables multiple clinical sites to collaboratively train a series of models in privacy, with each model tailored to the individual distribution of each site. Existing PFL methods rarely consider the personalization of multi-headed self-attention, and do not account for appearance diversity and instrument shape similarity, both inherent in surgical scenes. We thus propose PFedSIS, a novel PFL method with visual trait priors for SIS, incorporating global-personalized disentanglement (GPD), appearance-regulation personalized enhancement (APE), and shape-similarity global enhancement (SGE), to boost SIS performance in each site. GPD represents the first attempt at head- wise assignment for multi-headed self-attention personalization. To preserve the unique appearance representation of each site and gradually leverage the inter-site difference, APE introduces appearance regulation and provides customized layer- wise aggregation solutions via hypernetworks for each site's personalized parameters. The mutual shape information of instruments is maintained and shared via SGE, which enhances the cross-style shape consistency on the image level and computes the shape-similarity contribution of each site on the prediction level for updating the global parameters. PFedSIS outperforms state-of-the-art methods with +1.51% Dice, +2.11% IoU, -2.79 ASSD, -15.55 HD95 performance gains. The corresponding code and models are available at https://github.com/wzjialang/PFedSIS .
| Type: | Article |
|---|---|
| Title: | Personalizing Federated Instrument Segmentation With Visual Trait Priors in Robotic Surgery |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/tbme.2025.3526667 |
| Publisher version: | https://doi.org/10.1109/tbme.2025.3526667 |
| 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: | Personalized federated learning, multi-headed self-attention, hypernetwork, appearance regulation, shape similarity |
| 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 Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10203181 |
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