State-Change Learning for Prediction of Future Events in Endoscopic Videos

arXiv:2510.12904 · cs.CV · Submitted 2025-10-14 · Read on arXiv

cs.CV

Submitted: 2025-10-14

Updated: 2026-09-17

Code: https://github.com/open-mmlab/mmaction2

Terminology

• Clustering — 27× in this paper · explained in 2 episodes
• Benchmark — 18× in this paper · explained in 2 episodes
• Remaining Surgery Duration — 17× in this paper
• Distillation — 16× in this paper · explained in 2 episodes
• Accuracy — 14× in this paper · explained in 1 episode
• Ablation — 12× in this paper · explained in 1 episode
• Generalization — 12× in this paper · explained in 13 episodes
• Embeddings — 11× in this paper · explained in 2 episodes
• Foundation Model — 11× in this paper · explained in 3 episodes
• Foundation Models — 10× in this paper · explained in 7 episodes
• Transformer — 10× in this paper · explained in 2 episodes
• Teacher-Student Distillation — 8× in this paper · explained in 2 episodes
• Transfer Learning — 8× in this paper · explained in 10 episodes
• Downstream Tasks — 7× in this paper · explained in 2 episodes
• Self-Supervised Learning — 6× in this paper · explained in 8 episodes
• Temporal Reasoning — 6× in this paper · explained in 2 episodes
• Generalizability — 5× in this paper · explained in 6 episodes
• Phase Transitions — 4× in this paper · explained in 2 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Aggregation — 3× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Adjacency Matrix — 2× in this paper · explained in 1 episode
• Anticipative Video Transformer — 2× in this paper
• Computational overhead — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Hypergraph — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Segmentation Masks — 2× in this paper · explained in 2 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Wasserstein Distance — 2× in this paper · explained in 4 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Structured Reasoning — 1× in this paper · explained in 3 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: Challenges and methods
• SuPRA: Surgical Phase Recognition and Anticipation for Intra-Operative Planning
• How Attentive are Graph Attention Networks?
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets
• Hypergraph-Transformer (HGT) for Interactive Event Prediction in Laparoscopic and Robotic Surgery

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