Search Results for author: Xiaomeng Li

Found 31 papers, 14 papers with code

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

Free Lunch for Surgical Video Understanding by Distilling Self-Supervisions

no code implementations19 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 +1

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.

Centralized Adversarial Learning for Robust Deep Hashing

no code implementations18 Apr 2022 Xunguang Wang, Xu Yuan, Zheng Zhang, Guangming Lu, Xiaomeng Li

We, for the first time, attempt to design an effective adversarial learning with the min-max paradigm to improve the robustness of hashing networks by using the generated adversarial samples.

Adversarial Attack Image Retrieval +2

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

no code implementations16 Feb 2022 Zixun Wang, Xinpeng Ding, Wei Zhao, Xiaomeng Li

Moreover, our proposed approach can be used to clean noisy labels near boundaries and improve the performance of the current surgical phase recognition methods.

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.

Medical Image Segmentation Semantic Segmentation

Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation

no code implementations22 Nov 2021 Huimin Wu, Xiaomeng Li, Kwang-Ting Cheng

To enhance the representation learning, we propose a stage-adaptive contrastive learning method, including a boundary-aware contrastive loss to regularize the labeled images in the first stage and a prototype-aware contrastive loss to optimize both labeled and pseudo labeled images in the second stage.

Contrastive Learning Representation Learning +2

Exploring Segment-level Semantics for Online Phase Recognition from Surgical Videos

1 code implementation22 Nov 2021 Xinpeng Ding, Xiaomeng Li

Automatic surgical phase recognition plays a vital role in robot-assisted surgeries.

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

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 Lesion Segmentation +1

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.

Medical Image Segmentation Semantic Segmentation

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

5 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.

Left Atrium Segmentation Medical Image Segmentation +1

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 +3

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

1 code implementation28 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.

Lesion Segmentation Liver Segmentation +4

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.

Automatic Liver And Tumor Segmentation Lesion Segmentation +2

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