ArticleIEEE robotics and automation letters2025
Few-Shot Magnet Localization Using Sim-to-Real Transfer Learning.
Article in IEEE robotics and automation letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Path planning for fracture reduction robots incorporating physiological tissue response and safety-oriented optimization.International journal of computer assisted radiology and surgery · 2026Article
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4 authors.
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Abstract
Magnet localization is widely used for real-time medical instrument tracking because it offers a radiation-free alternative to fluoroscopy. While physics-model-based tracking methods are standard, localization accuracy can suffer owing to imperfect physical assumptions and computational inefficiencies in iterative convergence. This paper proposes a learning-based method that uses a single, compact neural network to achieve efficient magnetic localization with low computation cost. The proposed method achieves few-shot generalization through transfer learning based on a simulation dataset. Mean magnet position and orientation errors are 1.66 mm and 2.15° in a 100×100×100mm workspace using only four 3-channel sensors, providing a 2× improvement compared to a magnetic model-based method. A real-time catheter tracking experiment demonstrates that the efficacy of the method for practical medical applications.
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