arXiv Computer Vision
LapaFM: A Laparoscopic Foundation Model via Multi-Stage Continual Learning
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arXiv Machine Learning
Sep 11
Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge
The FedSurg Challenge is the first international effort to evaluate Federated Learning (FL) for surgical vision, using a multi‑center dataset of laparoscopic appendectomies. Three participant models were tested for generalization to an unseen clinical center and for center‑specific adaptation, compared against centralized, Swarm Learning, and parameter‑efficient fine‑tuning baselines. The study found that temporal modeling most consistently improves generalization, but overall performance remains low (26.31% F1‑score on the unseen center), highlighting the need for structured personalized FL and revealing limitations of current approaches.
arXiv Computer Vision
Aug 25
SurgTEMP: Temporal-Aware Surgical Video Question Answering with Text-guided Visual Memory for Laparoscopic Cholecystectomy
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....
Hugging Face Trending Papers
Jul 6
MergeSurv: Merging-Based Continual Learning for Survival Analysis on Whole-Slide Images
Survival analysis on Whole Slide Images (WSIs) is important in computational pathology for prognosis estimation and treatment planning. However, existing survival models are typically trained independently for each cancer cohort, making continual adaptation computationally expensive for gigapixel-scale WSIs.
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 26
Hierarchical Prototype-Memory Adaptation of SAM for Surgical Instrument Segmentation
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 Computer Vision
Sep 2
SurgiATM: A Physics-Guided Plug-and-Play Model for Deep Learning-Based Smoke Removal in Laparoscopic Surgery
The paper introduces SurgiATM, a lightweight physics-guided module for removing surgical smoke from laparoscopic endoscopic frames. It integrates a physics-based atmospheric model with a data-driven deep learning approach via a Mixture-of-Experts output stage, using a Laplacian-like error distribution to model smoke. SurgiATM adds only two hyperparameters and no extra trainable weights, enabling easy integration into existing desmoking architectures and improving accuracy and stability across multiple datasets and procedures.