Search Results for author: Xiaomeng Li

Found 58 papers, 37 papers with code

GL-Fusion: Global-Local Fusion Network for Multi-view Echocardiogram Video Segmentation

1 code implementation20 Sep 2023 Ziyang Zheng, Jiewen Yang, Xinpeng Ding, Xiaowei Xu, Xiaomeng Li

Additionally, a Multi-view Local-based Fusion Module (MLFM) is designed to extract correlations of cardiac structures from different views.

Video Segmentation Video Semantic Segmentation

GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation

1 code implementation20 Sep 2023 Jiewen Yang, Xinpeng Ding, Ziyang Zheng, Xiaowei Xu, Xiaomeng Li

This paper studies the unsupervised domain adaption (UDA) for echocardiogram video segmentation, where the goal is to generalize the model trained on the source domain to other unlabelled target domains.

Graph Matching Unsupervised Domain Adaptation +2

HiLM-D: Towards High-Resolution Understanding in Multimodal Large Language Models for Autonomous Driving

no code implementations11 Sep 2023 Xinpeng Ding, Jianhua Han, Hang Xu, Wei zhang, Xiaomeng Li

For the first time, we leverage singular multimodal large language models (MLLMs) to consolidate multiple autonomous driving tasks from videos, i. e., the Risk Object Localization and Intention and Suggestion Prediction (ROLISP) task.

Autonomous Driving Object Localization

CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say No

1 code implementation23 Aug 2023 Hualiang Wang, Yi Li, Huifeng Yao, Xiaomeng Li

Subsequently, we introduce two loss functions: the image-text binary-opposite loss and the text semantic-opposite loss, which we use to teach CLIPN to associate images with no prompts, thereby enabling it to identify unknown samples.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

Radiomics-Informed Deep Learning for Classification of Atrial Fibrillation Sub-Types from Left-Atrium CT Volumes

1 code implementation14 Aug 2023 Weihang Dai, Xiaomeng Li, Taihui Yu, Di Zhao, Jun Shen, Kwang-Ting Cheng

Furthermore, we ensure complementary information is learned by deep and radiomic features by designing a novel feature de-correlation loss.

feature selection

Fundus-Enhanced Disease-Aware Distillation Model for Retinal Disease Classification from OCT Images

1 code implementation1 Aug 2023 Lehan Wang, Weihang Dai, Mei Jin, Chubin Ou, Xiaomeng Li

Our framework enhances the OCT model during training by utilizing unpaired fundus images and does not require the use of fundus images during testing, which greatly improves the practicality and efficiency of our method for clinical use.

Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification

1 code implementation27 Jul 2023 Marawan Elbatel, Hualiang Wang, Robert Martí, Huazhu Fu, Xiaomeng Li

Existing federated methods under highly imbalanced datasets primarily focus on optimizing a global model without incorporating the intra-class variations that can arise in medical imaging due to different populations, findings, and scanners.

Federated Learning Image Classification +2

FSDiffReg: Feature-wise and Score-wise Diffusion-guided Unsupervised Deformable Image Registration for Cardiac Images

1 code implementation22 Jul 2023 Yi Qin, Xiaomeng Li

Specifically, FDG uses the diffusion model's multi-scale semantic features to guide the generation of the deformation field.

Image Registration

Morphology-inspired Unsupervised Gland Segmentation via Selective Semantic Grouping

1 code implementation22 Jul 2023 Qixiang Zhang, Yi Li, Cheng Xue, Xiaomeng Li

In this paper, we make a first attempt to explore a deep learning method for unsupervised gland segmentation, where no manual annotations are required.

Unsupervised Semantic Segmentation

DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentation

1 code implementation22 Jul 2023 Haonan Wang, Xiaomeng Li

Aiming to solve this issue, we present a novel Dual-debiased Heterogeneous Co-training (DHC) framework for semi-supervised 3D medical image segmentation.

Image Segmentation Semantic Segmentation +1

FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition

no code implementations27 May 2023 Marawan Elbatel, Robert Martí, Xiaomeng Li

Through these modules, FoPro-KD achieves significant improvements in performance on long-tailed medical image classification benchmarks, demonstrating the potential of leveraging the learned frequency patterns from pre-trained models to enhance transfer learning and compression of large pre-trained models for feasible deployment.

Image Classification Knowledge Distillation +2

Dynamic Data Augmentation via MCTS for Prostate MRI Segmentation

1 code implementation25 May 2023 Xinyue Xu, Yuhan Hsi, Haonan Wang, Xiaomeng Li

However, manually configuring a generic augmentation combination and parameters for different datasets is non-trivial due to inconsistent acquisition approaches and data distributions.

Data Augmentation MRI segmentation

Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation

2 code implementations15 Apr 2023 Huimin Wu, Xiaomeng Li, Yiqun Lin, Kwang-Ting Cheng

This study investigates barely-supervised medical image segmentation where only few labeled data, i. e., single-digit cases are available.

Image Segmentation Pancreas Segmentation +2

CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks

2 code implementations12 Apr 2023 Yi Li, Hualiang Wang, Yiqun Duan, Xiaomeng Li

Contrastive Language-Image Pre-training (CLIP) is a powerful multimodal large vision model that has demonstrated significant benefits for downstream tasks, including many zero-shot learning and text-guided vision tasks.

Interactive Segmentation Open Vocabulary Semantic Segmentation +2

Vessel-Promoted OCT to OCTA Image Translation by Heuristic Contextual Constraints

1 code implementation13 Mar 2023 Shuhan LI, Dong Zhang, Xiaomeng Li, Chubin Ou, Lin An, Yanwu Xu, Kwang-Ting Cheng

In this paper, we propose a novel framework, TransPro, that translates 3D Optical Coherence Tomography (OCT) images into exclusive 3D OCTA images using an image translation pattern.


Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction

1 code implementation12 Mar 2023 Yiqun Lin, Zhongjin Luo, Wei Zhao, Xiaomeng Li

In this paper, we formulate the CT volume as a continuous intensity field and develop a novel DIF-Net to perform high-quality CBCT reconstruction from extremely sparse (fewer than 10) projection views at an ultrafast speed.

Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networks

1 code implementation15 Feb 2023 Weihang Dai, Xiaomeng Li, Kwang-Ting Cheng

In this work, we propose a novel approach to semi-supervised regression, namely Uncertainty-Consistent Variational Model Ensembling (UCVME), which improves training by generating high-quality pseudo-labels and uncertainty estimates for heteroscedastic regression.

Age Estimation regression

Cyclical Self-Supervision for Semi-Supervised Ejection Fraction Prediction from Echocardiogram Videos

1 code implementation20 Oct 2022 Weihang Dai, Xiaomeng Li, Xinpeng Ding, Kwang-Ting Cheng

We also introduce teacher-student distillation to distill the information from LV segmentation masks into an end-to-end LVEF regression model that only requires video inputs.

regression Video Prediction

TripleE: Easy Domain Generalization via Episodic Replay

1 code implementation4 Oct 2022 Xiaomeng Li, Hongyu Ren, Huifeng Yao, Ziwei Liu

In this paper, we propose TripleE, and the main idea is to encourage the network to focus on training on subsets (learning with replay) and enlarge the data space in learning on subsets.

Domain Generalization

FreeSeg: Free Mask from Interpretable Contrastive Language-Image Pretraining for Semantic Segmentation

no code implementations27 Sep 2022 Yi Li, Huifeng Yao, Hualiang Wang, Xiaomeng Li

We call the proposed framework as FreeSeg, where the mask is freely available from raw feature map of pretraining model.

Retrieval Semantic Segmentation +1

Exploring Visual Interpretability for Contrastive Language-Image Pre-training

1 code implementation15 Sep 2022 Yi Li, Hualiang Wang, Yiqun Duan, Hang Xu, Xiaomeng Li

For this problem, we propose the Explainable Contrastive Language-Image Pre-training (ECLIP), which corrects the explainability via the Masked Max Pooling.

Retrieval text similarity

Learning Shadow Correspondence for Video Shadow Detection

no code implementations30 Jul 2022 Xinpeng Ding, Jingweng Yang, Xiaowei Hu, Xiaomeng Li

We further design a new evaluation metric to evaluate the temporal stability of the video shadow detection results.

Shadow Detection

Sub-cluster-aware Network for Few-shot Skin Disease Classification

1 code implementation3 Jul 2022 Shuhan LI, Xiaomeng Li, Xiaowei Xu, Kwang-Ting Cheng

Specifically, SCAN follows a dual-branch framework, where the first branch is to learn class-wise features to distinguish different skin diseases, and the second one aims to learn features which can effectively partition each class into several groups so as to preserve the sub-clustered structure within each class.

Classification Clustering +2

Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images

1 code implementation14 Jun 2022 Yi Li, Yiduo Yu, Yiwen Zou, Tianqi Xiang, Xiaomeng Li

Existing weakly-supervised semantic segmentation methods in computer vision achieve degenerative results for gland segmentation, since the characteristics and problems of glandular datasets are different from general object datasets.

Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation

Free Lunch for Surgical Video Understanding by Distilling Self-Supervisions

1 code implementation19 May 2022 Xinpeng Ding, Ziwei Liu, Xiaomeng Li

Our key insight is to distill knowledge from publicly available models trained on large generic datasets4 to facilitate the self-supervised learning of surgical videos.

Contrastive Learning Self-Supervised Learning +2

Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation

1 code implementation7 May 2022 Yiqun Lin, Huifeng Yao, Zezhong Li, Guoyan Zheng, Xiaomeng Li

Our framework leverages label distribution to encourage the network to put more effort into learning cartilage parts.

CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval

1 code implementation18 Apr 2022 Xunguang Wang, Yiqun Lin, Xiaomeng Li

On the one hand, CgAT generates the worst adversarial examples as augmented data by maximizing the Hamming distance between the hash codes of the adversarial examples and the center codes.

Adversarial Attack Adversarial Defense +4

RSCFed: Random Sampling Consensus Federated Semi-supervised Learning

1 code implementation CVPR 2022 Xiaoxiao Liang, Yiqun Lin, Huazhu Fu, Lei Zhu, Xiaomeng Li

In this paper, we present a Random Sampling Consensus Federated learning, namely RSCFed, by considering the uneven reliability among models from fully-labeled clients, fully-unlabeled clients or partially labeled clients.

Federated Learning

Less is More: Surgical Phase Recognition from Timestamp Supervision

1 code implementation16 Feb 2022 Xinpeng Ding, Xinjian Yan, Zixun Wang, Wei Zhao, Jian Zhuang, Xiaowei Xu, Xiaomeng Li

Our study uncovers unique insights of surgical phase recognition with timestamp supervisions: 1) timestamp annotation can reduce 74% annotation time compared with the full annotation, and surgeons tend to annotate those timestamps near the middle of phases; 2) extensive experiments demonstrate that our method can achieve competitive results compared with full supervision methods, while reducing manual annotation cost; 3) less is more in surgical phase recognition, i. e., less but discriminative pseudo labels outperform full but containing ambiguous frames; 4) the proposed UATD can be used as a plug and play method to clean ambiguous labels near boundaries between phases, and improve the performance of the current surgical phase recognition methods.

Surgical phase recognition

Enhancing Pseudo Label Quality for Semi-Supervised Domain-Generalized Medical Image Segmentation

1 code implementation21 Jan 2022 Huifeng Yao, Xiaowei Hu, Xiaomeng Li

With these augmentations as perturbations, we feed the input to a confidence-aware cross pseudo supervision network to measure the variance of pseudo labels and regularize the network to learn with more confident pseudo labels.

Image Segmentation Medical Image Segmentation +2

Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation

1 code implementation22 Nov 2021 Huimin Wu, Xiaomeng Li, Kwang-Ting Cheng

A stage-adaptive contrastive learning method is proposed, containing a boundary-aware contrastive loss that takes advantage of the labeled images in the first stage, as well as a prototype-aware contrastive loss to optimize both labeled and pseudo labeled images in the second stage.

Contrastive Learning Image Segmentation +3

Improved Heatmap-based Landmark Detection

no code implementations12 Oct 2021 Huifeng Yao, Ziyu Guo, Yatao Zhang, Xiaomeng Li

This paper proposes a landmark detection network for detecting sutures in endoscopic pictures, which solves the problem of a variable number of suture points in the images.

Metal Artifact Reduction in 2D CT Images with Self-supervised Cross-domain Learning

no code implementations28 Sep 2021 Lequan Yu, Zhicheng Zhang, Xiaomeng Li, Hongyi Ren, Wei Zhao, Lei Xing

We then design a novel FBP reconstruction loss to encourage the network to generate more perfect completion results and a residual-learning-based image refinement module to reduce the secondary artifacts in the reconstructed CT images.

Image Reconstruction Metal Artifact Reduction

Support-Set Based Cross-Supervision for Video Grounding

no code implementations ICCV 2021 Xinpeng Ding, Nannan Wang, Shiwei Zhang, De Cheng, Xiaomeng Li, Ziyuan Huang, Mingqian Tang, Xinbo Gao

The contrastive objective aims to learn effective representations by contrastive learning, while the caption objective can train a powerful video encoder supervised by texts.

Contrastive Learning Video Grounding

Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation

no code implementations5 Apr 2021 Cheng Xue, Qiao Deng, Xiaomeng Li, Qi Dou, Pheng Ann Heng

To deal with the high inter-rater variability, the study of imperfect label has great significance in medical image segmentation tasks.

Image Segmentation Medical Image Segmentation +1

Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images

no code implementations5 Apr 2021 Cheng Xue, Lei Zhu, Huazhu Fu, Xiaowei Hu, Xiaomeng Li, Hai Zhang, Pheng Ann Heng

The BD modules learn additional breast lesion boundary map to enhance the boundary quality of a segmentation result refinement.

Boundary Detection Image Segmentation +2

Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images

no code implementations16 Sep 2020 Lequan Yu, Zhicheng Zhang, Xiaomeng Li, Lei Xing

Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance.

Computed Tomography (CT) Image Generation +2

Self-supervised Feature Learning via Exploiting Multi-modal Data for Retinal Disease Diagnosis

1 code implementation21 Jul 2020 Xiaomeng Li, Mengyu Jia, Md Tauhidul Islam, Lequan Yu, Lei Xing

The automatic diagnosis of various retinal diseases from fundus images is important to support clinical decision-making.

Decision Making

Effects of Regional Trade Agreement to Local and Global Trade Purity Relationships

no code implementations29 May 2020 Siyu Huang, Wensha Gou, Hongbo Cai, Xiaomeng Li, Qinghua Chen

In addition, we apply the network to reflect the purity of the trade relations among countries.

The 'Letter' Distribution in the Chinese Language

no code implementations26 May 2020 Qinghua Chen, Yan Wang, Mengmeng Wang, Xiaomeng Li

In addition, we collected Chinese literature corpora for different historical periods from the Tang Dynasty to the present, and we dismantled the Chinese written language into three kinds of basic particles: characters, strokes and constructive parts.

CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading

1 code implementation4 Nov 2019 Xiaomeng Li, Xiao-Wei Hu, Lequan Yu, Lei Zhu, Chi-Wing Fu, Pheng-Ann Heng

In this paper, we present a novel cross-disease attention network (CANet) to jointly grade DR and DME by exploring the internal relationship between the diseases with only image-level supervision.

Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

7 code implementations16 Jul 2019 Lequan Yu, Shujun Wang, Xiaomeng Li, Chi-Wing Fu, Pheng-Ann Heng

We design a novel uncertainty-aware scheme to enable the student model to gradually learn from the meaningful and reliable targets by exploiting the uncertainty information.

Image Segmentation Left Atrium Segmentation +2

Revisiting Metric Learning for Few-Shot Image Classification

no code implementations6 Jul 2019 Xiaomeng Li, Lequan Yu, Chi-Wing Fu, Meng Fang, Pheng-Ann Heng

However, the importance of feature embedding, i. e., exploring the relationship among training samples, is neglected.

Classification Few-Shot Image Classification +4

Difficulty-aware Meta-learning for Rare Disease Diagnosis

no code implementations30 Jun 2019 Xiaomeng Li, Lequan Yu, Yueming Jin, Chi-Wing Fu, Lei Xing, Pheng-Ann Heng

Rare diseases have extremely low-data regimes, unlike common diseases with large amount of available labeled data.

General Classification Lesion Classification +2

Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation

no code implementations28 Feb 2019 Xiaomeng Li, Lequan Yu, Hao Chen, Chi-Wing Fu, Lei Xing, Pheng-Ann Heng

In this paper, we present a novel semi-supervised method for medical image segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data.

Image Segmentation Lesion Segmentation +5

The Liver Tumor Segmentation Benchmark (LiTS)

6 code implementations13 Jan 2019 Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivantik, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yu, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.

Benchmarking Computed Tomography (CT) +2

Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model

no code implementations12 Aug 2018 Xiaomeng Li, Lequan Yu, Hao Chen, Chi-Wing Fu, Pheng-Ann Heng

In this paper, we present a novel semi-supervised method for skin lesion segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data.

Lesion Segmentation Skin Lesion Segmentation

Deeply Supervised Rotation Equivariant Network for Lesion Segmentation in Dermoscopy Images

1 code implementation8 Jul 2018 Xiaomeng Li, Lequan Yu, Chi-Wing Fu, Pheng-Ann Heng

Our best model achieves 77. 23\%(JA) on the test dataset, outperforming the state-of-the-art challenging methods and further demonstrating the effectiveness of our proposed deeply supervised rotation equivariant segmentation network.

Lesion Segmentation Skin Lesion Segmentation

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes

1 code implementation21 Sep 2017 Xiaomeng Li, Hao Chen, Xiaojuan Qi, Qi Dou, Chi-Wing Fu, Pheng Ann Heng

Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.

 Ranked #1 on Liver Segmentation on LiTS2017 (Dice metric)

Automatic Liver And Tumor Segmentation Image Segmentation +3

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