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:2609.14624v1 Announce Type: new Abstract: Reliable workflow transition detection is important for context-aware surgical assistance and downstream decision support. However, online surgical pha...
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...
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.
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.
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.
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:2608.31065v1 Announce Type: new Abstract: Surgical phase recognition is key to context-aware computer-assisted feedback in vitreoretinal procedures, yet the scarcity of synchronized multimodal...
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....
arXiv:2608. 20154v1 Announce Type: new Abstract: Minimally invasive colorectal surgeries (MIS-CRS) are characterised by significant variability and inconsistent outcomes.
The paper presents a segmentation pipeline for brain metastases in both pre‑ and post‑treatment cases using a 5‑fold nnU‑Net ResEnc‑L ensemble trained on 1,296 four‑modality cases. A rule‑based post‑processing cascade improves the lesion‑wise Dice similarity coefficient (LW‑DSC) for enhancing tumour, tumour core, whole tumour, and resection cavity sub‑regions, achieving LW‑DSC scores of 0.733, 0.751, 0.713, and 0.549 respectively on the official validation leaderboard. The authors conduct a five‑fold out‑of‑fold analysis to validate the robustness of each post‑processing stage, provide a mechanistic explanation of LW‑DSC behaviour, and report thirteen negative results that challenge common intuitions, with all code released under Apache‑2.0.
arXiv:2606. 29247v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models represent a promising direction for embodied intelligence in surgical robotics.
TEDi is a Temporal memory-Enhanced and Denoising Transformer designed for surgical instrument segmentation. It introduces a query-level memory bank with a memory search enhancement encoder to incorporate discriminative representations from past frames, and a temporal consistency denoising module that builds a cross‑frame semantic anchor to stabilize predictions. Experiments on EndoVis 2017 and EndoVis 2018 show that TEDi outperforms existing state‑of‑the‑art methods, indicating its effectiveness for computer‑assisted surgery.