arXiv:2608. 20154v1 Announce Type: new Abstract: Minimally invasive colorectal surgeries (MIS-CRS) are characterised by significant variability and inconsistent outcomes.
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...
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.
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.
arXiv:2610.34218v1 Announce Type: new Abstract: Spatial localization of surgical instruments through bounding-box annotations is a foundational component of surgical scene understanding, supporting d...
Spatial localization of surgical instruments through bounding-box annotations is a foundational component of surgical scene understanding, supporting detection, tracking, interaction analysis, and gro...
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...
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.
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.
arXiv:2609.21402v1 Announce Type: new Abstract: Surgical instrument segmentation (SIS) plays a critical role in robotic assistance and surgical workflow analysis. However, most existing SIS methods f...
arXiv:2608.24541v1 Announce Type: new Abstract: Surgical instrument segmentation (SIS) is fundamental for computer-assisted surgery, where reliable instrument masks enable precise scene understanding...
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...