A hierarchical prompt tree with self-reflection graph propagation enables positive forward and backward knowledge transfer in incremental surgical instrument segmentation, improving over baselines by more than 5% and 11% on two benchmarks.
Cholecseg8k: a semantic segmentation dataset for laparoscopic cholecystectomy based on cholec80
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
citation-role summary
dataset 3
background 1
citation-polarity summary
representative citing papers
SAM 3 outperforms SAM 2 under click prompting for zero-shot 3D medical segmentation across 16 datasets and 54 structures, with fewer failure modes in prompt-frame over-segmentation and prediction retention.
Introduces the triplet segmentation task, CholecTriplet-Seg dataset with over 30,000 frames, and TargetFusionNet architecture extending Mask2Former for instance-level grounding of surgical <instrument, verb, target> triplets.
EndoGSim integrates MLLM-guided material initialization with 4D Gaussian Splatting and differentiable Material Point Method to achieve physics-aware 4D reconstruction and simulation of endoscopic scenes.
A multi-stage Delphi consensus with 92 experts catalogs widespread validation pitfalls in surgical AI video analysis across data, metrics, and reporting, supported by a systematic review and empirical experiments.
DEX is a modular network using dynamically activated experts and a group-EMA director to learn emergent modular representations for multi-modality medical vision foundation models, evaluated on a new 4M-image benchmark across 10 modalities and 26 downstream tasks.
DenseTRF adapts texture-aware representations via slot attention for unsupervised improvement of cross-domain generalization in surgical dense prediction tasks.
The paper summarizes results from the SurgToolLoc and SurgVU challenges held at MICCAI conferences from 2022 to 2025.
citing papers explorer
-
Unlocking Positive Transfer in Incrementally Learning Surgical Instruments: A Self-reflection Hierarchical Prompt Framework
A hierarchical prompt tree with self-reflection graph propagation enables positive forward and backward knowledge transfer in incremental surgical instrument segmentation, improving over baselines by more than 5% and 11% on two benchmarks.
-
Comparing SAM 2 and SAM 3 for Zero-Shot Segmentation of 3D Medical Data
SAM 3 outperforms SAM 2 under click prompting for zero-shot 3D medical segmentation across 16 datasets and 54 structures, with fewer failure modes in prompt-frame over-segmentation and prediction retention.
-
Grounding Surgical Action Triplets with Instrument Instance Segmentation: A Dataset and Target-Aware Fusion Approach
Introduces the triplet segmentation task, CholecTriplet-Seg dataset with over 30,000 frames, and TargetFusionNet architecture extending Mask2Former for instance-level grounding of surgical <instrument, verb, target> triplets.
-
EndoGSim: Physics-Aware 4D Dynamic Endoscopic Scene Simulations via MLLM-Guided Gaussian Splatting
EndoGSim integrates MLLM-guided material initialization with 4D Gaussian Splatting and differentiable Material Point Method to achieve physics-aware 4D reconstruction and simulation of endoscopic scenes.
-
Current validation practice undermines surgical AI development
A multi-stage Delphi consensus with 92 experts catalogs widespread validation pitfalls in surgical AI video analysis across data, metrics, and reporting, supported by a systematic review and empirical experiments.
-
Learning Emergent Modular Representations in Multi-modality Medical Vision Foundation Models
DEX is a modular network using dynamically activated experts and a group-EMA director to learn emergent modular representations for multi-modality medical vision foundation models, evaluated on a new 4M-image benchmark across 10 modalities and 26 downstream tasks.
-
DenseTRF: Texture-Aware Unsupervised Representation Adaptation for Surgical Scene Dense Prediction
DenseTRF adapts texture-aware representations via slot attention for unsupervised improvement of cross-domain generalization in surgical dense prediction tasks.
-
Intuitive Surgical SurgToolLoc and SurgVU Challenges Results: 2022-2025
The paper summarizes results from the SurgToolLoc and SurgVU challenges held at MICCAI conferences from 2022 to 2025.