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Found 24 papers, 18 papers with code
Surgical Action Planning with Large Language Models
Large Language Models (LLMs) show promise in understanding surgical video content but remain underexplored for predictive decision-making in SAP, as they focus mainly on retrospective analysis.
ETSM: Automating Dissection Trajectory Suggestion and Confidence Map-Based Safety Margin Prediction for Robot-assisted Endoscopic Submucosal Dissection
Robot-assisted Endoscopic Submucosal Dissection (ESD) improves the surgical procedure by providing a more comprehensive view through advanced robotic instruments and bimanual operation, thereby enhancing dissection efficiency and accuracy.
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection
Conclusion: The PDZSeg model effectively utilizes visual prompts to enhance segmentation performance and user experience, supported by the novel ESD-DZSeg dataset as a benchmark for dissection zone segmentation in ESD.
Benchmarking Robustness of Endoscopic Depth Estimation with Synthetically Corrupted Data
To tackle this issue, our study presents a benchmark for assessing the robustness of endoscopic depth estimation models.
SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation
The recent Segment Anything Model (SAM) 2 has demonstrated remarkable foundational competence in semantic segmentation, with its memory mechanism and mask decoder further addressing challenges in video tracking and object occlusion, thereby achieving superior results in interactive segmentation for both images and videos.
A Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery
As a crucial and intricate task in robotic minimally invasive surgery, reconstructing surgical scenes using stereo or monocular endoscopic video holds immense potential for clinical applications.
PitVQA: Image-grounded Text Embedding LLM for Visual Question Answering in Pituitary Surgery
This paper introduces PitVQA, a novel dataset specifically designed for VQA in endonasal pituitary surgery and PitVQA-Net, an adaptation of the GPT2 with a novel image-grounded text embedding for surgical VQA.
An Efficient MLP-based Point-guided Segmentation Network for Ore Images with Ambiguous Boundary
The precise segmentation of ore images is critical to the successful execution of the beneficiation process.
Privacy-Preserving Synthetic Continual Semantic Segmentation for Robotic Surgery
The problem becomes worse when it limits releasing the dataset of the old instruments for the old model due to privacy concerns and the unavailability of the data for the new or updated version of the instruments for the continual learning model.
Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting
In the realm of robot-assisted minimally invasive surgery, dynamic scene reconstruction can significantly enhance downstream tasks and improve surgical outcomes.
SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge
Surgical tool segmentation and action recognition are fundamental building blocks in many computer-assisted intervention applications, ranging from surgical skills assessment to decision support systems.
SAM Meets Robotic Surgery: An Empirical Study on Generalization, Robustness and Adaptation
Our extensive evaluation results reveal that although SAM shows remarkable zero-shot generalization ability with bounding box prompts, it struggles to segment the whole instrument with point-based prompts and unprompted settings.
Rectifying Noisy Labels with Sequential Prior: Multi-Scale Temporal Feature Affinity Learning for Robust Video Segmentation
Therefore, Temporal Feature Affinity Learning (TFAL) is devised to indicate possible noisy labels by evaluating the affinity between pixels in two adjacent frames.
Ranked #2 on
Video Polyp Segmentation
on SUN-SEG-Easy
Generalizing Surgical Instruments Segmentation to Unseen Domains with One-to-Many Synthesis
In this work, we mitigate data-related issues by efficiently leveraging minimal source images to generate synthetic surgical instrument segmentation datasets and achieve outstanding generalization performance on unseen real domains.
Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image Segmentation
Accurate and robust medical image segmentation is fundamental and crucial for enhancing the autonomy of computer-aided diagnosis and intervention systems.
S$^2$ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation
Furthermore, to produce reliable mixed pseudo labels, which enhance the effectiveness of ensemble learning, we introduce a novel adaptive pixel-wise fusion technique based on the entropy guidance from the spatial and spectral branches.
Surgical tool classification and localization: results and methods from the MICCAI 2022 SurgToolLoc challenge
Unfortunately, obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task.
SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective
In this empirical study, we investigate the robustness and zero-shot generalizability of the SAM in the domain of robotic surgery in various settings of (i) prompted vs. unprompted; (ii) bounding box vs. points-based prompt; (iii) generalization under corruptions and perturbations with five severity levels; and (iv) state-of-the-art supervised model vs. SAM.
Confidence-Aware Paced-Curriculum Learning by Label Smoothing for Surgical Scene Understanding
In this work, we design a paced curriculum by label smoothing (P-CBLS) using paced learning with uniform label smoothing (ULS) for classification tasks and fuse uniform and spatially varying label smoothing (SVLS) for semantic segmentation tasks in a curriculum manner.
Task-Aware Asynchronous Multi-Task Model with Class Incremental Contrastive Learning for Surgical Scene Understanding
Conclusion: The proposed multi-task model was able to adapt to domain shifts, incorporate novel instruments in the target domain, and perform tool-tissue interaction detection and report generation on par with single-task models.
Rethinking Surgical Captioning: End-to-End Window-Based MLP Transformer Using Patches
Surgical captioning plays an important role in surgical instruction prediction and report generation.
Rethinking Surgical Instrument Segmentation: A Background Image Can Be All You Need
Our empirical analysis suggests that without the high cost of data collection and annotation, we can achieve decent surgical instrument segmentation performance.
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge.
Ranked #1 on
Action Triplet Recognition
on CholecT50 (Challenge)
(using extra training data)
Class-Incremental Domain Adaptation with Smoothing and Calibration for Surgical Report Generation
To adapt incremental classes and extract domain invariant features, a class-incremental (CI) learning method with supervised contrastive (SupCon) loss is incorporated with a feature extractor.
