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By incorporating language guidance into federated learning, SurgFed tackles the long-standing problem of tissue and task heterogeneity in surgical video understanding, leading to improved segmentation and depth estimation across diverse surgical settings.
Ditch the fiducial markers: SurgCalib achieves sub-5mm 3D tool-tip error in surgical robot hand-eye calibration using Gaussian Splatting, opening the door to sterile, markerless OR workflows.
Current vision-language models are surprisingly bad at surgical safety reasoning, failing to integrate phase information to identify safe operative zones, but a new RLHF-tuned model closes the gap.
Achieve state-of-the-art ECG analysis by disentangling modality-specific biases and capturing spatial-temporal dependencies, outperforming existing multimodal approaches.
MedVAR leapfrogs existing methods by achieving state-of-the-art medical image generation with an autoregressive architecture designed for both speed and scalability.
