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Dynamic integration of multimodal knowledge boosts the recognition of rare surgical actions, overcoming critical optimization conflicts in robot-assisted surgery.
By alternating between segmentation and curve modeling, A2ONet achieves more accurate liver landmark detection in laparoscopic images, even under poor illumination.
Achieve more stable and accurate alignment of preoperative liver models with intraoperative laparoscopic views by explicitly modeling reliable 2D-3D geometric correspondences supported by latent evidence.
