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

• URL: http://arxiv.org/abs/2510.12904v1
• Date: Tue, 14 Oct 2025 18:25:03 GMT
• Title: State-Change Learning for Prediction of Future Events in Endoscopic Videos
• Abstract summary: We introduce SurgFUTR, implementing this through a teacher-student architecture.<n>Video clips are compressed into state representations via Sinkhorn-Knopp clustering.<n>We establish SFPBench with five prediction tasks spanning short-term (triplets, events) and long-term (remaining surgery duration, phase and step transitions) horizons. Experiments across four datasets and three procedures show consistent improvements.
• Score: 8.258852831142017
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Surgical future prediction, driven by real-time AI analysis of surgical video, is critical for operating room safety and efficiency. It provides actionable insights into upcoming events, their timing, and risks-enabling better resource allocation, timely instrument readiness, and early warnings for complications (e.g., bleeding, bile duct injury). Despite this need, current surgical AI research focuses on understanding what is happening rather than predicting future events. Existing methods target specific tasks in isolation, lacking unified approaches that span both short-term (action triplets, events) and long-term horizons (remaining surgery duration, phase transitions). These methods rely on coarse-grained supervision while fine-grained surgical action triplets and steps remain underexplored. Furthermore, methods based only on future feature prediction struggle to generalize across different surgical contexts and procedures. We address these limits by reframing surgical future prediction as state-change learning. Rather than forecasting raw observations, our approach classifies state transitions between current and future timesteps. We introduce SurgFUTR, implementing this through a teacher-student architecture. Video clips are compressed into state representations via Sinkhorn-Knopp clustering; the teacher network learns from both current and future clips, while the student network predicts future states from current videos alone, guided by our Action Dynamics (ActDyn) module. We establish SFPBench with five prediction tasks spanning short-term (triplets, events) and long-term (remaining surgery duration, phase and step transitions) horizons. Experiments across four datasets and three procedures show consistent improvements. Cross-procedure transfer validates generalizability.

Related papers

• UniMPA: A Unified Memory-Prediction-Action Model via Action-Grounded Transition Modeling [73.35395707747456]
  Recent advances in Vision-Language-Action models have improved robotic manipulation, yet observation-to-action learning remains limited by a fundamental transition realizability gap.<n>A visually plausible predicted future observation does not necessarily correspond to a physically realizable transition.<n>A historically executable action pattern may not necessarily realize the intended transition in the current scene and therefore requires context-aware adaptation.
  arXiv  Detail & Related papers  (2026-09-10T17:45:03Z)
• Multimodal Optimal Transport for Unsupervised Temporal Segmentation in Surgical Robotics [2.582839864045357]
  Recognizing surgical phases and steps from video is a fundamental problem in computer-assisted interventions.<n>Recent approaches increasingly rely on large-scale pre-training on thousands of labeled surgical videos, followed by zero-shot transfer to specific procedures.<n>We propose Text-Augmented Action Optimal Transport (TASOT), an unsupervised method for surgical phase and step recognition.
  arXiv  Detail & Related papers  (2026-02-27T16:15:58Z)
• Fostering Video Reasoning via Next-Event Prediction [61.70045315542766]
  We propose next-event prediction (NEP) as a learning task that harnesses future video segments as a rich, self-supervised signal to foster temporal reasoning.<n>To support this task, we curate V1-33K, a dataset comprising 33,000 automatically extracted video segments spanning diverse real-world scenarios.<n> Experiments validate that NEP offers a scalable and effective training paradigm for fostering temporal reasoning in MLLMs.
  arXiv  Detail & Related papers  (2025-05-28T15:13:34Z)
• SWAG: Long-term Surgical Workflow Prediction with Generative-based Anticipation [43.95663763660549]
  We propose SWAG (Surgical Anticipative Generation), a framework that combines phase recognition and anticipation.<n>This paper investigates two distinct decoding methods - single-pass (SP) and auto-regressive (AR)<n>We propose a novel embedding approach using class transition probabilities to enhance the accuracy of phase anticipation.
  arXiv  Detail & Related papers  (2024-12-25T09:29:57Z)
• Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation [9.329654505950199]
  We propose an adaptive graph learning framework for surgical workflow anticipation based on a novel spatial representation.<n>We develop a multi-horizon objective that balances learning objectives for different time horizons, allowing for unconstrained predictions.
  arXiv  Detail & Related papers  (2024-12-09T12:53:08Z)
• Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation [51.222684687924215]
  Surgical video-language pretraining faces unique challenges due to the knowledge domain gap and the scarcity of multi-modal data.<n>We propose a hierarchical knowledge augmentation approach and a novel Procedure-Encoded Surgical Knowledge-Augmented Video-Language Pretraining framework to tackle these issues.
  arXiv  Detail & Related papers  (2024-09-30T22:21:05Z)
• Hypergraph-Transformer (HGT) for Interactive Event Prediction in Laparoscopic and Robotic Surgery [47.47211257890948]
  We propose a predictive neural network that is capable of understanding and predicting critical interactive aspects of surgical workflow from intra-abdominal video.<n>We verify our approach on established surgical datasets and applications, including the detection and prediction of action triplets.<n>Our results demonstrate the superiority of our approach compared to unstructured alternatives.
  arXiv  Detail & Related papers  (2024-02-03T00:58:05Z)
• Multiscale Video Pretraining for Long-Term Activity Forecasting [67.06864386274736]
  Multiscale Video Pretraining learns robust representations for forecasting by learning to predict contextualized representations of future video clips over multiple timescales. MVP is based on our observation that actions in videos have a multiscale nature, where atomic actions typically occur at a short timescale and more complex actions may span longer timescales. Our comprehensive experiments across the Ego4D and Epic-Kitchens-55/100 datasets demonstrate that MVP out-performs state-of-the-art methods by significant margins.
  arXiv  Detail & Related papers  (2023-07-24T14:55:15Z)
• GLSFormer : Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos [57.93194315839009]
  We propose a vision transformer-based approach to learn temporal features directly from sequence-level patches. We extensively evaluate our approach on two cataract surgery video datasets, Cataract-101 and D99, and demonstrate superior performance compared to various state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-07-20T17:57:04Z)
• Behavioral Intention Prediction in Driving Scenes: A Survey [70.53285924851767]
  Behavioral Intention Prediction (BIP) simulates a human consideration process and fulfills the early prediction of specific behaviors. This work provides a comprehensive review of BIP from the available datasets, key factors and challenges, pedestrian-centric and vehicle-centric BIP approaches, and BIP-aware applications.
  arXiv  Detail & Related papers  (2022-11-01T11:07:37Z)
• Review of Video Predictive Understanding: Early ActionRecognition and Future Action Prediction [39.966828592322315]
  Action prediction is a major sub-area of video predictive understanding. Various mathematical tools are widely adopted jointly with computer vision techniques for these two tasks. Structures that rely on deep convolutional neural networks and recurrent neural networks have been extensively proposed for improving the performance of existing vision tasks.
  arXiv  Detail & Related papers  (2021-07-11T22:46:52Z)
• SUrgical PRediction GAN for Events Anticipation [38.65189355224683]
  We used a novel GAN formulation that sampled the future surgical phases trajectory conditioned, on past laparoscopic video frames. We demonstrated its effectiveness in inferring and predicting the progress of laparoscopic cholecystectomy videos. We surveyed surgeons to evaluate the plausibility of these predicted trajectories.
  arXiv  Detail & Related papers  (2021-05-10T19:56:45Z)
• TTPP: Temporal Transformer with Progressive Prediction for Efficient Action Anticipation [46.28067541184604]
  Video action anticipation aims to predict future action categories from observed frames. Current state-of-the-art approaches mainly resort to recurrent neural networks to encode history information into hidden states. This paper proposes a simple yet efficient Temporal Transformer with Progressive Prediction framework.
  arXiv  Detail & Related papers  (2020-03-07T07:59:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.