arXiv AI
InnerLogic-Surg: An Open Surgical Foundation Model via Continual Pretraining
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arXiv Computer Vision
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SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos
SurgMotion is a video-native foundation model that replaces pixel-level reconstruction with latent motion prediction for surgical video analysis. It introduces motion-guided masked prediction, spatiotemporal affinity self-distillation, and spatiotemporal feature diversity regularization to focus on semantically meaningful regions and avoid representation collapse. Trained on SurgMotion-15M, the largest surgical video dataset, it outperforms state-of-the-art methods across 17 benchmarks, improving workflow recognition, action triplet recognition, skill assessment, polyp segmentation, and depth estimation.
arXiv Computer Vision
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DART: Depth-as-Target Pretraining for Surgical Vision Foundation Models
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arXiv Computer Vision
Oct 8
Beyond Masks and Trajectories: Flow-Guided Latent Action Injection for Stable Surgical Video Generation
arXiv:2610.09800v1 Announce Type: new Abstract: Surgical video generation holds substantial potential for surgical education, simulation, and data augmentation, yet generating surgical videos with re...
arXiv Machine Learning
Aug 3
Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation
arXiv:2607. 29509v1 Announce Type: cross Abstract: Effective multi-organ segmentation in surgical data requires learning the intricate anatomical features and alleviating the challenge of class imbalance, which results from relatively lower proportions of small and limitedly exposed structures.
arXiv Computation and Language
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arXiv AI
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DaX: Learning General Pathology Representations Across Scales
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