Search Results for author: Lequan Yu

Found 42 papers, 18 papers with code

Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training

no code implementations10 May 2022 Cheng Xue, Lequan Yu, Pengfei Chen, Qi Dou, Pheng-Ann Heng

In this paper, we propose a novel collaborative training paradigm with global and local representation learning for robust medical image classification from noisy-labeled data to combat the lack of high quality annotated medical data.

Image Classification Representation Learning

CD$^2$-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning

no code implementations8 Apr 2022 Yiqing Shen, Yuyin Zhou, Lequan Yu

Federated learning (FL) is a distributed learning paradigm that enables multiple clients to collaboratively learn a shared global model.

Federated Learning

Learning to Bootstrap for Combating Label Noise

1 code implementation9 Feb 2022 Yuyin Zhou, Xianhang Li, Fengze Liu, Xuxi Chen, Lequan Yu, Cihang Xie, Matthew P. Lungren, Lei Xing

Specifically, our method dynamically adjusts the per-sample importance weight between the real observed labels and pseudo-labels, where the weights are efficiently determined in a meta process.

Ranked #5 on Image Classification on Clothing1M (using clean data) (using extra training data)

Image Classification Representation Learning

Rethinking Client Reweighting for Selfish Federated Learning

no code implementations29 Sep 2021 Ruichen Luo, Shoubo Hu, Lequan Yu

To this end, we study a new $\textit{selfish}$ variant of federated learning, in which the ultimate objective is to learn a model with optimal performance on internal clients $\textit{alone}$ instead of all clients.

Federated Learning Image Classification +3

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

HCDG: A Hierarchical Consistency Framework for Domain Generalization on Medical Image Segmentation

no code implementations13 Sep 2021 Yijun Yang, Shujun Wang, Pheng-Ann Heng, Lequan Yu

In this paper, we present a novel Hierarchical Consistency framework for Domain Generalization (HCDG) by ensembling Extrinsic Consistency and Intrinsic Consistency.

Data Augmentation Domain Generalization +2

DualNorm-UNet: Incorporating Global and Local Statistics for Robust Medical Image Segmentation

1 code implementation29 Mar 2021 Junfei Xiao, Lequan Yu, Lei Xing, Alan Yuille, Yuyin Zhou

However, BN only calculates the global statistics at the batch level, and applies the same affine transformation uniformly across all spatial coordinates, which would suppress the image contrast of different semantic structures.

Medical Image Segmentation Semantic Segmentation

TransCT: Dual-path Transformer for Low Dose Computed Tomography

1 code implementation28 Feb 2021 Zhicheng Zhang, Lequan Yu, Xiaokun Liang, Wei Zhao, Lei Xing

Low dose computed tomography (LDCT) has attracted more and more attention in routine clinical diagnosis assessment, therapy planning, etc., which can reduce the dose of X-ray radiation to patients.

Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac Segmentation

1 code implementation7 Jan 2021 Kang Li, Shujun Wang, Lequan Yu, Pheng-Ann Heng

In this way, the dual teacher models would transfer acquired inter- and intra-domain knowledge to the student model for further integration and exploitation.

Cardiac Segmentation Domain Adaptation +1

DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets

no code implementations13 Oct 2020 Shujun Wang, Lequan Yu, Kang Li, Xin Yang, Chi-Wing Fu, Pheng-Ann Heng

Our DoFE framework dynamically enriches the image features with additional domain prior knowledge learned from multi-source domains to make the semantic features more discriminative.

Domain Generalization Semantic Segmentation

Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge Distillation

no code implementations4 Oct 2020 Kang Li, Lequan Yu, Shujun Wang, Pheng-Ann Heng

Considering multi-modality data with the same anatomic structures are widely available in clinic routine, in this paper, we aim to exploit the prior knowledge (e. g., shape priors) learned from one modality (aka., assistant modality) to improve the segmentation performance on another modality (aka., target modality) to make up annotation scarcity.

Cardiac Segmentation Knowledge Distillation +1

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

Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization

no code implementations ECCV 2020 Shujun Wang, Lequan Yu, Caizi Li, Chi-Wing Fu, Pheng-Ann Heng

To this end, we present a new domain generalization framework that learns how to generalize across domains simultaneously from extrinsic relationship supervision and intrinsic self-supervision for images from multi-source domains.

Domain Generalization Metric Learning +2

Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge

no code implementations4 Jul 2020 Yue Sun, Kun Gao, Zhengwang Wu, Zhihao Lei, Ying WEI, Jun Ma, Xiaoping Yang, Xue Feng, Li Zhao, Trung Le Phan, Jitae Shin, Tao Zhong, Yu Zhang, Lequan Yu, Caizi Li, Ramesh Basnet, M. Omair Ahmad, M. N. S. Swamy, Wenao Ma, Qi Dou, Toan Duc Bui, Camilo Bermudez Noguera, Bennett Landman, Ian H. Gotlib, Kathryn L. Humphreys, Sarah Shultz, Longchuan Li, Sijie Niu, Weili Lin, Valerie Jewells, Gang Li, Dinggang Shen, Li Wang

Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.

Brain Segmentation

Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation

no code implementations28 Jun 2020 Yingda Xia, Dong Yang, Zhiding Yu, Fengze Liu, Jinzheng Cai, Lequan Yu, Zhuotun Zhu, Daguang Xu, Alan Yuille, Holger Roth

Experiments on the NIH pancreas segmentation dataset and a multi-organ segmentation dataset show state-of-the-art performance of the proposed framework on semi-supervised medical image segmentation.

Pancreas Segmentation Semantic Segmentation +3

Deep Mining External Imperfect Data for Chest X-ray Disease Screening

no code implementations6 Jun 2020 Luyang Luo, Lequan Yu, Hao Chen, Quande Liu, Xi Wang, Jiaqi Xu, Pheng-Ann Heng

Recent researches have demonstrated that performance bottleneck exists in joint training on different CXR datasets, and few made efforts to address the obstacle.

General Classification Thoracic Disease Classification

Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model

1 code implementation15 May 2020 Quande Liu, Lequan Yu, Luyang Luo, Qi Dou, Pheng Ann Heng

It is a consistency-based method which exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations, and leverages a self-ensembling model to produce high-quality consistency targets for the unlabeled data.

Classification General Classification +1

MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data

2 code implementations9 Feb 2020 Quande Liu, Qi Dou, Lequan Yu, Pheng Ann Heng

However, the prostate MRIs from different sites present heterogeneity due to the differences in scanners and imaging protocols, raising challenges for effective ways of aggregating multi-site data for network training.

Transfer Learning

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.

Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound

1 code implementation10 Oct 2019 Haoran Dou, Xin Yang, Jikuan Qian, Wufeng Xue, Hao Qin, Xu Wang, Lequan Yu, Shujun Wang, Yi Xiong, Pheng-Ann Heng, Dong Ni

In this study, we propose a novel reinforcement learning (RL) framework to automatically localize fetal brain standard planes in 3D US.

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

Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation

1 code implementation26 Jun 2019 Shujun Wang, Lequan Yu, Kang Li, Xin Yang, Chi-Wing Fu, Pheng-Ann Heng

The cross-domain discrepancy (domain shift) hinders the generalization of deep neural networks to work on different domain datasets. In this work, we present an unsupervised domain adaptation framework, called Boundary and Entropy-driven Adversarial Learning (BEAL), to improve the OD and OC segmentation performance, especially on the ambiguous boundary regions.

Frame Semantic Segmentation +1

Unsupervised Detection of Distinctive Regions on 3D Shapes

no code implementations5 May 2019 Xianzhi Li, Lequan Yu, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng

This paper presents a novel approach to learn and detect distinctive regions on 3D shapes.

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

Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation

no code implementations20 Feb 2019 Shujun Wang, Lequan Yu, Xin Yang, Chi-Wing Fu, Pheng-Ann Heng

In this paper, we present a novel patchbased Output Space Adversarial Learning framework (pOSAL) to jointly and robustly segment the OD and OC from different fundus image datasets.

Unsupervised Domain Adaptation

3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training

no code implementations29 Nov 2018 Yingda Xia, Fengze Liu, Dong Yang, Jinzheng Cai, Lequan Yu, Zhuotun Zhu, Daguang Xu, Alan Yuille, Holger Roth

Meanwhile, a fully-supervised method based on our approach achieved state-of-the-art performances on both the LiTS liver tumor segmentation and the Medical Segmentation Decathlon (MSD) challenge, demonstrating the robustness and value of our framework, even when fully supervised training is feasible.

Medical Image Segmentation Tumor Segmentation

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

EC-Net: an Edge-aware Point set Consolidation Network

no code implementations ECCV 2018 Lequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng

In this paper, we present the first deep learning based edge-aware technique to facilitate the consolidation of point clouds.

Surface Reconstruction

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

PU-Net: Point Cloud Upsampling Network

3 code implementations CVPR 2018 Lequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng

Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data.

Point Cloud Super Resolution

Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets

2 code implementations2 Aug 2017 Lequan Yu, Jie-Zhi Cheng, Qi Dou, Xin Yang, Hao Chen, Jing Qin, Pheng-Ann Heng

Second, it avoids learning redundant feature maps by encouraging feature reuse and hence requires fewer parameters to achieve high performance, which is essential for medical applications with limited training data.

Fine-grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images

no code implementations6 Dec 2016 Xin Yang, Lequan Yu, Lingyun Wu, Yi Wang, Dong Ni, Jing Qin, Pheng-Ann Heng

Additionally, our approach is general and can be extended to other medical image segmentation tasks, where boundary incompleteness is one of the main challenges.

Medical Image Segmentation Semantic Segmentation

VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation

3 code implementations21 Aug 2016 Hao Chen, Qi Dou, Lequan Yu, Pheng-Ann Heng

Recently deep residual learning with residual units for training very deep neural networks advanced the state-of-the-art performance on 2D image recognition tasks, e. g., object detection and segmentation.

Brain Segmentation Object Detection +1

3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes

no code implementations3 Jul 2016 Qi Dou, Hao Chen, Yueming Jin, Lequan Yu, Jing Qin, Pheng-Ann Heng

Automatic liver segmentation from CT volumes is a crucial prerequisite yet challenging task for computer-aided hepatic disease diagnosis and treatment.

Liver Segmentation

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

no code implementations CVPR 2016 Hao Chen, Xiaojuan Qi, Lequan Yu, Pheng-Ann Heng

The morphology of glands has been used routinely by pathologists to assess the malignancy degree of adenocarcinomas.

Multi-Task Learning

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