arXiv Computer Vision
Semantic RGB--Depth Based Surgical Skill Assessment in Microscopic Stereo Videos
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arXiv Computer Vision
Sep 22
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
Aug 25
SurgTEMP: Temporal-Aware Surgical Video Question Answering with Text-guided Visual Memory for Laparoscopic Cholecystectomy
arXiv:2603.29962v4 Announce Type: replace Abstract: Surgical procedures are inherently complex and risky, requiring extensive expertise and constant focus to navigate evolving intraoperative scenes....
Hugging Face Trending Papers
Jun 25
Temporally Consistent Label Interpolation for Robust Surgical Multi-Task Learning under Challenging Conditions
Effective multi-task learning for surgical scene understanding is fundamentally hindered by annotation granularity mismatch; temporal workflow tasks such as phase recognition, step recognition and anticipation benefit from dense frame-level supervision, whereas pixel-level spatial tasks including instrument segmentation and action recognition are only sparsely annotated on selected keyframes due to prohibitive labeling costs. This supervision imbalance undermines shared representation learning and limits joint optimization across heterogeneous surgical tasks.
Hugging Face Trending Papers
Jun 22
Expert Consensus on Criteria for the Automated Assessment of Laparoscopic Camera Navigation
Background: Laparoscopic camera navigation (LCN) is a critical skill, yet its current assessment typically relies on manual rating systems which are time-consuming and difficult to scale. Automated feedback could significantly enhance surgical training by providing immediate, standardized metrics.
arXiv Computer Vision
Sep 7
DART: Depth-as-Target Pretraining for Surgical Vision Foundation Models
DART is a new RGB‑D pretraining method for surgical vision foundation models that incorporates pseudo‑labeled depth maps as a pixel‑space reconstruction target during training. By adding a depth reconstruction head to DINOv2’s masked iBOT framework, DART improves representation quality without affecting downstream RGB‑only fine‑tuning or inference. Across eight surgical benchmarks—including segmentation, depth estimation, and image‑level recognition—DART outperforms both natural‑image and in‑domain baselines, demonstrating that geometric pseudo‑labels can strengthen foundation model pretraining without extra labels or inference cost.
arXiv AI
Aug 19
Explainable AI-Powered Framework for Video-Based Skill Assessment in Cataract Surgery
The paper introduces an explainable AI framework for automated skill assessment in cataract surgery, leveraging the world’s largest dataset of 2,000 surgical videos. Using advanced computer vision and signal‑processing techniques, the system extracts ten objective motion‑based metrics that correlate strongly with expert subjective ratings, achieving up to 87% accuracy. The framework’s explainability distinguishes it from prior opaque classification tools, offering transparent, quantitative performance indicators that could complement or replace traditional scoring methods.