Summary of Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery, by Jialang Xu et al.
Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery
by Jialang Xu, Jiacheng Wang, Lequan Yu, Danail Stoyanov, Yueming Jin, Evangelos B. Mazomenos
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO); Medical Physics (physics.med-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to personalized federated learning (PFL) for surgical instrument segmentation (SIS). Existing PFL methods are limited by their inability to consider the personalization of multi-headed self-attention, appearance diversity, and instrument shape similarity. The proposed method, PFedSIS, incorporates global-personalized disentanglement (GPD), appearance-regulation personalized enhancement (APE), and shape-similarity global enhancement (SGE) to improve SIS performance in each site. PFedSIS outperforms state-of-the-art methods with significant gains in Dice score, IoU, ASSD, and HD95. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PFedSIS is a new way for hospitals to work together on computer vision tasks without sharing their data. It helps machines learn to recognize surgical instruments from different sites by considering how each site’s instruments look. This approach does better than others in this field, especially when it comes to things like precision and accuracy. |
Keywords
» Artificial intelligence » Federated learning » Precision » Self attention
