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20232026
most citedSAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective

12 citations · 39 across the 30 of their papers we have counts for

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11 papers · 1 filter

cs.CV2023

Spatial-wise Dynamic Distillation for MLP-like Efficient Visual Fault Detection of Freight Trains

Yang Zhang, Huilin Pan, Mingying Li +3

Despite the successful application of convolutional neural networks (CNNs) in object detection tasks, their efficiency in detecting faults from freight train images remains inadequ…

eess.IV2023★ 3 cited

SAM Meets Robotic Surgery: An Empirical Study on Generalization, Robustness and Adaptation

An Wang, Mobarakol Islam, Mengya Xu +2

The Segment Anything Model (SAM) serves as a fundamental model for semantic segmentation and demonstrates remarkable generalization capabilities across a wide range of downstream s…

cs.CV2023

Semi-supervised Learning for Segmentation of Bleeding Regions in Video Capsule Endoscopy

Hechen Li, Yanan Wu, Long Bai +3

In the realm of modern diagnostic technology, video capsule endoscopy (VCE) is a standout for its high efficacy and non-invasive nature in diagnosing various gastrointestinal (GI)…

eess.IV2023

Landmark Detection using Transformer Toward Robot-assisted Nasal Airway Intubation

Tianhang Liu, Hechen Li, Long Bai +4

Robot-assisted airway intubation application needs high accuracy in locating targets and organs. Two vital landmarks, nostrils and glottis, can be detected during the intubation to…

eess.IV2023

LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion

Long Bai, Tong Chen, Yanan Wu +3

Wireless capsule endoscopy (WCE) is a painless and non-invasive diagnostic tool for gastrointestinal (GI) diseases. However, due to GI anatomical constraints and hardware manufactu…

cs.CV2023

Rectifying Noisy Labels with Sequential Prior: Multi-Scale Temporal Feature Affinity Learning for Robust Video Segmentation

Beilei Cui, Minqing Zhang, Mengya Xu +3

Noisy label problems are inevitably in existence within medical image segmentation causing severe performance degradation. Previous segmentation methods for noisy label problems on…

eess.IV2023

Generalizing Surgical Instruments Segmentation to Unseen Domains with One-to-Many Synthesis

An Wang, Mobarakol Islam, Mengya Xu +1

Despite their impressive performance in various surgical scene understanding tasks, deep learning-based methods are frequently hindered from deploying to real-world surgical applic…

eess.IV2023★ 1 cited

Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image Segmentation

An Wang, Mobarakol Islam, Mengya Xu +1

Accurate and robust medical image segmentation is fundamental and crucial for enhancing the autonomy of computer-aided diagnosis and intervention systems. Medical data collection n…

eess.IV2023★ 2 cited

SME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation

An Wang, Mengya Xu, Yang Zhang +2

Fully-supervised polyp segmentation has accomplished significant triumphs over the years in advancing the early diagnosis of colorectal cancer. However, label-efficient solutions f…

cs.CV2023★ 6 cited

Intuitive Surgical SurgToolLoc and SurgVU Challenges Results: 2022-2025

Aneeq Zia, Max Berniker, Rogerio Garcia Nespolo +153

Robotic assisted (RA) surgery promises to transform surgical intervention. Intuitive Surgical is committed to fostering these changes and the machine learning models and algorithms…

eess.IV2023★ 12 cited

SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective

An Wang, Mobarakol Islam, Mengya Xu +2

Segment Anything Model (SAM) is a foundation model for semantic segmentation and shows excellent generalization capability with the prompts. In this empirical study, we investigate…