arXiv Computer Vision
TTDF: A Two-Stage Framework for Reliable Surgical Phase Transition Detection
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arXiv AI
Aug 19
SurgicalMamba: Dual-Path SSD with State Regramming for Online Surgical Phase Recognition
SurgicalMamba introduces a dual‑path Structured State‑Space Duality (SSD) model with two novel mechanisms—state regramming and intensity‑modulated stepping—to improve online surgical phase recognition. State regramming rotates the carried state at chunk boundaries based on content, separating repeated views that occur in different phases, while intensity‑modulated stepping adjusts decay rates at phase transitions to better capture phase length variability. The approach achieves state‑of‑the‑art accuracy and Jaccard scores on seven public benchmarks, running at 312.88 fps on a single GPU, and its rotation mechanism also boosts multi‑query associative recall in other domains.
arXiv Computer Vision
Aug 25
Large-Small Model Collaboration for Zero-Shot Surgical Phase Recognition
The paper introduces LaST, a large-small collaborative framework for zero-shot surgical phase recognition. It combines a foundation model that generates frame-level phase priors with a lightweight model that refines predictions through iterative temporal refinement, dynamic quality control, and dual-model cross-learning. Experiments show LaST outperforms baseline and state-of-the-art methods, achieving significant accuracy gains on unseen clinical domains.
arXiv Computer Vision
Sep 17
LaSeD: Label-Semantic Self-Distillation for Visual-Only Surgical Phase Recognition
arXiv:2609.18971v1 Announce Type: new Abstract: Surgical phase recognition maps each video frame to a clinically meaningful workflow phase, supporting context-aware assistance, documentation, and pos...
arXiv Computer Vision
Sep 14
OphBiWSSD: Scaling Temporal Action Localization in Ophthalmic Surgeries with Bidirectional Weight-tied State Space Duality
OphBiWSSD is a new framework for temporal action localization in ophthalmic surgeries that uses Bidirectional State Space Duality to avoid the quadratic memory cost of Transformers. It employs a weight‑tied selective scan that incorporates both past and future surgical context, enabling linear‑time global synthesis of non‑causal temporal cues. On the OphNet benchmark, OphBiWSSD achieves state‑of‑the‑art mean Average Precisions of 44.42 % for phases and 43.08 % for operations, outperforming baselines by 6.80 % and 6.66 % respectively.
arXiv Computer Vision
Sep 18
State-Change Learning for Prediction of Future Events in Endoscopic Videos
The paper introduces SurgFUTR, a state‑change learning framework for predicting future events in endoscopic videos. Instead of forecasting raw observations, it classifies transitions between current and future states using a teacher‑student architecture and an Action Dynamics module. The authors also present SFPBench, a benchmark with five short‑ and long‑term prediction tasks, and demonstrate consistent improvements across multiple datasets and procedures, including cross‑procedure transfer.
arXiv Computer Vision
Aug 21
Artificial Intelligence for Workflow Analysis in Colorectal Surgery: A Multicentric, Cross-Procedural Development and Generalization Study
arXiv:2608. 20154v1 Announce Type: new Abstract: Minimally invasive colorectal surgeries (MIS-CRS) are characterised by significant variability and inconsistent outcomes.