Search Results for author: Mengya Xu

Found 17 papers, 14 papers with code

An Efficient MLP-based Point-guided Segmentation Network for Ore Images with Ambiguous Boundary

1 code implementation27 Feb 2024 Guodong Sun, Yuting Peng, Le Cheng, Mengya Xu, An Wang, Bo Wu, Hongliang Ren, Yang Zhang

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

1 code implementation8 Feb 2024 Mengya Xu, Mobarakol Islam, Long Bai, Hongliang Ren

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.

Continual Learning Continual Semantic Segmentation +3

Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting

2 code implementations29 Jan 2024 Yiming Huang, Beilei Cui, Long Bai, Ziqi Guo, Mengya Xu, Mobarakol Islam, Hongliang Ren

In the realm of robot-assisted minimally invasive surgery, dynamic scene reconstruction can significantly enhance downstream tasks and improve surgical outcomes.

Dynamic Reconstruction Monocular Depth Estimation +2

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

no code implementations14 Aug 2023 An Wang, Mobarakol Islam, Mengya Xu, Yang Zhang, Hongliang Ren

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.

Semantic Segmentation Zero-shot Generalization

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

1 code implementation12 Jul 2023 Beilei Cui, Minqing Zhang, Mengya Xu, An Wang, Wu Yuan, Hongliang Ren

Therefore, Temporal Feature Affinity Learning (TFAL) is devised to indicate possible noisy labels by evaluating the affinity between pixels in two adjacent frames.

Image Segmentation Medical Image Segmentation +4

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

1 code implementation28 Jun 2023 An Wang, Mobarakol Islam, Mengya Xu, Hongliang Ren

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.

Scene Understanding

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

1 code implementation6 Jun 2023 An Wang, Mobarakol Islam, Mengya Xu, Hongliang Ren

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

Domain Adaptation Image Segmentation +4

S$^2$ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation

1 code implementation1 Jun 2023 An Wang, Mengya Xu, Yang Zhang, Mobarakol Islam, Hongliang Ren

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.

Ensemble Learning Image Segmentation +4

SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective

no code implementations28 Apr 2023 An Wang, Mobarakol Islam, Mengya Xu, Yang Zhang, Hongliang Ren

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.

Semantic Segmentation Zero-shot Generalization

Confidence-Aware Paced-Curriculum Learning by Label Smoothing for Surgical Scene Understanding

1 code implementation22 Dec 2022 Mengya Xu, Mobarakol Islam, Ben Glocker, Hongliang Ren

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.

Multi-Label Classification Scene Understanding +1

Task-Aware Asynchronous Multi-Task Model with Class Incremental Contrastive Learning for Surgical Scene Understanding

1 code implementation28 Nov 2022 Lalithkumar Seenivasan, Mobarakol Islam, Mengya Xu, Chwee Ming Lim, Hongliang Ren

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.

Contrastive Learning Decision Making +4

Rethinking Surgical Instrument Segmentation: A Background Image Can Be All You Need

2 code implementations23 Jun 2022 An Wang, Mobarakol Islam, Mengya Xu, Hongliang Ren

Our empirical analysis suggests that without the high cost of data collection and annotation, we can achieve decent surgical instrument segmentation performance.

Domain Adaptation Incremental Learning +2

Class-Incremental Domain Adaptation with Smoothing and Calibration for Surgical Report Generation

1 code implementation23 Jul 2021 Mengya Xu, Mobarakol Islam, Chwee Ming Lim, Hongliang Ren

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.

Domain Adaptation Few-Shot Learning +1

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