Search Results for author: Lequan Yu

Found 86 papers, 46 papers with code

MedAgentBoard: Benchmarking Multi-Agent Collaboration with Conventional Methods for Diverse Medical Tasks

no code implementations18 May 2025 Yinghao Zhu, Ziyi He, Haoran Hu, Xiaochen Zheng, Xichen Zhang, Zixiang Wang, Junyi Gao, Liantao Ma, Lequan Yu

The rapid advancement of Large Language Models (LLMs) has stimulated interest in multi-agent collaboration for addressing complex medical tasks.

MuMA: 3D PBR Texturing via Multi-Channel Multi-View Generation and Agentic Post-Processing

no code implementations24 Mar 2025 Lingting Zhu, Jingrui Ye, Runze Zhang, Zeyu Hu, Yingda Yin, Lanjiong Li, Jinnan Chen, Shengju Qian, Xin Wang, Qingmin Liao, Lequan Yu

Current methods for 3D generation still fall short in physically based rendering (PBR) texturing, primarily due to limited data and challenges in modeling multi-channel materials.

3D Generation

Proxy-Tuning: Tailoring Multimodal Autoregressive Models for Subject-Driven Image Generation

no code implementations13 Mar 2025 Yi Wu, Lingting Zhu, Lei Liu, Wandi Qiao, Ziqiang Li, Lequan Yu, Bin Li

Multimodal autoregressive (AR) models, based on next-token prediction and transformer architecture, have demonstrated remarkable capabilities in various multimodal tasks including text-to-image (T2I) generation.

Image Generation

Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge Graphs

1 code implementation19 Feb 2025 Yushi Feng, Tsai Hor Chan, Guosheng Yin, Lequan Yu

Most of the existing augmentation methods overlook the context information inherited from the dataset as they rely solely on the graph structure for augmentation.

Data Augmentation Graph Learning +4

Large Images are Gaussians: High-Quality Large Image Representation with Levels of 2D Gaussian Splatting

1 code implementation13 Feb 2025 Lingting Zhu, Guying Lin, Jinnan Chen, Xinjie Zhang, Zhenchao Jin, Zhao Wang, Lequan Yu

While Implicit Neural Representations (INRs) have demonstrated significant success in image representation, they are often hindered by large training memory and slow decoding speed.

3D Reconstruction Novel View Synthesis

From Layers to States: A State Space Model Perspective to Deep Neural Network Layer Dynamics

no code implementations12 Feb 2025 Qinshuo Liu, Weiqin Zhao, Wei Huang, Yanwen Fang, Lequan Yu, Guodong Li

The depth of neural networks is a critical factor for their capability, with deeper models often demonstrating superior performance.

Image Classification State Space Models

Dynamic Entity-Masked Graph Diffusion Model for histopathological image Representation Learning

1 code implementation13 Dec 2024 Zhenfeng Zhuang, Min Cen, Yanfeng Li, Fangyu Zhou, Lequan Yu, Baptiste Magnier, Liansheng Wang

Moreover, the frequent lack of annotations in histopathology patch images has driven researchers to explore self-supervised learning methods like mask reconstruction for learning representations from large amounts of unlabeled data.

Representation Learning Self-Supervised Learning +1

Aligning Knowledge Concepts to Whole Slide Images for Precise Histopathology Image Analysis

1 code implementation27 Nov 2024 Weiqin Zhao, Ziyu Guo, Yinshuang Fan, Yuming Jiang, Maximus Yeung, Lequan Yu

Due to the large size and lack of fine-grained annotation, Whole Slide Images (WSIs) analysis is commonly approached as a Multiple Instance Learning (MIL) problem.

Language Modeling Language Modelling +2

Free Lunch in Pathology Foundation Model: Task-specific Model Adaptation with Concept-Guided Feature Enhancement

1 code implementation15 Nov 2024 Yanyan Huang, Weiqin Zhao, Yihang Chen, Yu Fu, Lequan Yu

However, these foundation models are usually designed for general-purpose pathology image analysis and may not be optimal for specific downstream tasks or cancer types.

model

CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis

no code implementations1 Nov 2024 Fuying Wang, Feng Wu, Yihan Tang, Lequan Yu

Integrating multimodal Electronic Health Records (EHR) data, such as numerical time series and free-text clinical reports, has great potential in predicting clinical outcomes.

cross-modal alignment Phenotype classification

ToolBridge: An Open-Source Dataset to Equip LLMs with External Tool Capabilities

1 code implementation8 Oct 2024 Zhenchao Jin, Mengchen Liu, Dongdong Chen, Lingting Zhu, Yunsheng Li, Lequan Yu

Through the integration of external tools, large language models (LLMs) such as GPT-4o and Llama 3. 1 significantly expand their functional capabilities, evolving from elementary conversational agents to general-purpose assistants.

Multi-sensor Learning Enables Information Transfer across Different Sensory Data and Augments Multi-modality Imaging

1 code implementation28 Sep 2024 Lingting Zhu, Yizheng Chen, Lianli Liu, Lei Xing, Lequan Yu

Here, we investigate a data-driven multi-modality imaging (DMI) strategy for synergetic imaging of CT and MRI.

Multi-task Heterogeneous Graph Learning on Electronic Health Records

1 code implementation14 Aug 2024 Tsai Hor Chan, Guosheng Yin, Kyongtae Bae, Lequan Yu

Learning electronic health records (EHRs) has received emerging attention because of its capability to facilitate accurate medical diagnosis.

Causal Inference Denoising +4

Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery

no code implementations6 Aug 2024 Jialang Xu, Jiacheng Wang, Lequan Yu, Danail Stoyanov, Yueming Jin, Evangelos B. Mazomenos

To preserve the unique appearance representation of each site and gradually leverage the inter-site difference, APE introduces appearance regulation and provides customized layer-wise aggregation solutions via hypernetworks for each site's personalized parameters.

Disentanglement Personalized Federated Learning

HERGen: Elevating Radiology Report Generation with Longitudinal Data

no code implementations21 Jul 2024 Fuying Wang, Shenghui Du, Lequan Yu

Radiology reports provide detailed descriptions of medical imaging integrated with patients' medical histories, while report writing is traditionally labor-intensive, increasing radiologists' workload and the risk of diagnostic errors.

Diagnostic

cDP-MIL: Robust Multiple Instance Learning via Cascaded Dirichlet Process

1 code implementation16 Jul 2024 Yihang Chen, Tsai Hor Chan, Guosheng Yin, Yuming Jiang, Lequan Yu

We then perform bag-level prediction with another Dirichlet process model on the bags, which imposes a natural regularization on learning to prevent overfitting and enhance generalizability.

Multiple Instance Learning

Completed Feature Disentanglement Learning for Multimodal MRIs Analysis

1 code implementation6 Jul 2024 Tianling Liu, Hongying Liu, Fanhua Shang, Lequan Yu, Tong Han, Liang Wan

Specifically, the CFD strategy not only identifies modality-shared and modality-specific features, but also decouples shared features among subsets of multimodal inputs, termed as modality-partial-shared features.

Disentanglement Mixture-of-Experts

Generative Enhancement for 3D Medical Images

2 code implementations19 Mar 2024 Lingting Zhu, Noel Codella, Dongdong Chen, Zhenchao Jin, Lu Yuan, Lequan Yu

Our method begins with a 2D slice, noted as the informed slice to serve the patient prior, and propagates the generation process using a 3D segmentation mask.

counterfactual Image Generation

Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining

1 code implementation5 Feb 2024 Jiarun Liu, Hao Yang, Hong-Yu Zhou, Yan Xi, Lequan Yu, Yizhou Yu, Yong Liang, Guangming Shi, Shaoting Zhang, Hairong Zheng, Shanshan Wang

However, it is challenging for existing methods to model long-range global information, where convolutional neural networks (CNNs) are constrained by their local receptive fields, and vision transformers (ViTs) suffer from high quadratic complexity of their attention mechanism.

Image Segmentation Mamba +3

EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting

1 code implementation21 Jan 2024 Lingting Zhu, Zhao Wang, Jiahao Cui, Zhenchao Jin, Guying Lin, Lequan Yu

Specifically, our approach incorporates deformation fields to handle dynamic scenes, depth-guided supervision with spatial-temporal weight masks to optimize 3D targets with tool occlusion from a single viewpoint, and surface-aligned regularization terms to capture the much better geometry.

3D Reconstruction

Single-Shot Plug-and-Play Methods for Inverse Problems

no code implementations22 Nov 2023 Yanqi Cheng, Lipei Zhang, Zhenda Shen, Shujun Wang, Lequan Yu, Raymond H. Chan, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero

In this work, we introduce Single-Shot PnP methods (SS-PnP), shifting the focus to solving inverse problems with minimal data.

MuST: Multimodal Spatiotemporal Graph-Transformer for Hospital Readmission Prediction

no code implementations11 Nov 2023 Yan Miao, Lequan Yu

Hospital readmission prediction is considered an essential approach to decreasing readmission rates, which is a key factor in assessing the quality and efficacy of a healthcare system.

Prediction Readmission Prediction

FetusMapV2: Enhanced Fetal Pose Estimation in 3D Ultrasound

no code implementations30 Oct 2023 Chaoyu Chen, Xin Yang, Yuhao Huang, Wenlong Shi, Yan Cao, Mingyuan Luo, Xindi Hu, Lei Zhue, Lequan Yu, Kejuan Yue, Yuanji Zhang, Yi Xiong, Dong Ni, Weijun Huang

However, accurately estimating the 3D fetal pose in US volume has several challenges, including poor image quality, limited GPU memory for tackling high dimensional data, symmetrical or ambiguous anatomical structures, and considerable variations in fetal poses.

Pose Estimation Self-Supervised Learning

IDRNet: Intervention-Driven Relation Network for Semantic Segmentation

1 code implementation NeurIPS 2023 Zhenchao Jin, Xiaowei Hu, Lingting Zhu, Luchuan Song, Li Yuan, Lequan Yu

Next, a deletion diagnostics procedure is conducted to model relations of these semantic-level representations via perceiving the network outputs and the extracted relations are utilized to guide the semantic-level representations to interact with each other.

Relation Relation Network +1

HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide Image Analysis

1 code implementation14 Sep 2023 Ziyu Guo, Weiqin Zhao, Shujun Wang, Lequan Yu

Considering that the information from different resolutions is complementary and can benefit each other during the learning process, we further design a novel Bidirectional Interaction block to establish communication between different levels within the WSI pyramids.

Graph Neural Network Prognosis +1

Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars

no code implementations27 Aug 2023 Weijia Feng, Lingting Zhu, Lequan Yu

However, the adoption of foundational models in the medical domain presents a challenge due to the difficulty and expense of labeling sufficient data for adaptation within hospital systems.

Brain Tumor Segmentation Image Segmentation +3

ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis

1 code implementation ICCV 2023 Yanyan Huang, Weiqin Zhao, Shujun Wang, Yu Fu, Yuming Jiang, Lequan Yu

In this paper, we propose the FIRST continual learning framework for WSI analysis, named ConSlide, to tackle the challenges of enormous image size, utilization of hierarchical structure, and catastrophic forgetting by progressive model updating on multiple sequential datasets.

Continual Learning Prognosis

Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation

1 code implementation21 Jul 2023 Qingyue Wei, Lequan Yu, Xianhang Li, Wei Shao, Cihang Xie, Lei Xing, Yuyin Zhou

Specifically, our approach first involves training a segmentation model on a small set of clean labeled images to generate initial labels for unlabeled data.

Image Segmentation Meta-Learning +4

Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis

no code implementations19 Jul 2023 Lingting Zhu, Zeyue Xue, Zhenchao Jin, Xian Liu, Jingzhen He, Ziwei Liu, Lequan Yu

This paradigm extends the 2D image diffusion model to a volumetric version with a slightly increasing number of parameters and computation, offering a principled solution for generic cross-modality 3D medical image synthesis.

Computational Efficiency Image Generation

Relabeling Minimal Training Subset to Flip a Prediction

1 code implementation22 May 2023 Jinghan Yang, Linjie Xu, Lequan Yu

When facing an unsatisfactory prediction from a machine learning model, users can be interested in investigating the underlying reasons and exploring the potential for reversing the outcome.

Binary Classification Prediction

HybridMIM: A Hybrid Masked Image Modeling Framework for 3D Medical Image Segmentation

1 code implementation18 Mar 2023 Zhaohu Xing, Lei Zhu, Lequan Yu, Zhiheng Xing, Liang Wan

Masked image modeling (MIM) with transformer backbones has recently been exploited as a powerful self-supervised pre-training technique.

Contrastive Learning Image Segmentation +3

Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation

1 code implementation CVPR 2023 Lingting Zhu, Xian Liu, Xuanyu Liu, Rui Qian, Ziwei Liu, Lequan Yu

In this work, we propose a novel diffusion-based framework, named Diffusion Co-Speech Gesture (DiffGesture), to effectively capture the cross-modal audio-to-gesture associations and preserve temporal coherence for high-fidelity audio-driven co-speech gesture generation.

Diversity Gesture Generation

MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery

1 code implementation CVPR 2023 Duowen Chen, Yunhao Bai, Wei Shen, Qingli Li, Lequan Yu, Yan Wang

Our strategy encourages unlabeled images to learn organ semantics in relative locations from the labeled images (cross-branch) and enhances the learning ability for small organs (within-branch).

Anatomy Data Augmentation +4

Domain-incremental Cardiac Image Segmentation with Style-oriented Replay and Domain-sensitive Feature Whitening

no code implementations9 Nov 2022 Kang Li, Lequan Yu, Pheng-Ann Heng

Particularly, we first present a style-oriented replay module to enable structure-realistic and memory-efficient reproduction of past data, and then incorporate the replayed past data to jointly optimize the model with current data to alleviate catastrophic forgetting.

Image Segmentation Incremental Learning +2

Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning

2 code implementations12 Oct 2022 Fuying Wang, Yuyin Zhou, Shujun Wang, Varut Vardhanabhuti, Lequan Yu

In this paper, we present a novel Multi-Granularity Cross-modal Alignment (MGCA) framework for generalized medical visual representation learning by harnessing the naturally exhibited semantic correspondences between medical image and radiology reports at three different levels, i. e., pathological region-level, instance-level, and disease-level.

Contrastive Learning cross-modal alignment +5

MCIBI++: Soft Mining Contextual Information Beyond Image for Semantic Segmentation

2 code implementations9 Sep 2022 Zhenchao Jin, Dongdong Yu, Zehuan Yuan, Lequan Yu

To this end, we propose a novel soft mining contextual information beyond image paradigm named MCIBI++ to further boost the pixel-level representations.

Segmentation Semantic Segmentation +1

Joint Prediction of Meningioma Grade and Brain Invasion via Task-Aware Contrastive Learning

1 code implementation4 Sep 2022 Tianling Liu, Wennan Liu, Lequan Yu, Liang Wan, Tong Han, Lei Zhu

Preoperative and noninvasive prediction of the meningioma grade is important in clinical practice, as it directly influences the clinical decision making.

Contrastive Learning Decision Making +2

NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation

1 code implementation31 Aug 2022 Zhaohu Xing, Lequan Yu, Liang Wan, Tong Han, Lei Zhu

Multi-modal MR imaging is routinely used in clinical practice to diagnose and investigate brain tumors by providing rich complementary information.

Brain Tumor Segmentation Decoder +3

You Should Look at All Objects

1 code implementation16 Jul 2022 Zhenchao Jin, Dongdong Yu, Luchuan Song, Zehuan Yuan, Lequan Yu

Feature pyramid network (FPN) is one of the key components for object detectors.

All

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 Medical Image Analysis +2

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 Medical Image Analysis

L2B: Learning to Bootstrap Robust Models for Combating Label Noise

1 code implementation CVPR 2024 Yuyin Zhou, Xianhang Li, Fengze Liu, Qingyue Wei, Xuxi Chen, Lequan Yu, Cihang Xie, Matthew P. Lungren, Lei Xing

Extensive experiments demonstrate that our method effectively mitigates the challenges of noisy labels, often necessitating few to no validation samples, and is well generalized to other tasks such as image segmentation.

Ranked #8 on Image Classification on Clothing1M (using clean data) (using extra training data)

Image Segmentation Learning with noisy labels +3

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 +5

All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation

1 code implementation28 Sep 2021 Zhe Xu, Yixin Wang, Donghuan Lu, Lequan Yu, Jiangpeng Yan, Jie Luo, Kai Ma, Yefeng Zheng, Raymond Kai-yu Tong

Observing this, we ask an unexplored but interesting question: can we exploit the unlabeled data via explicit real label supervision for semi-supervised training?

All Brain Tumor Segmentation +4

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

1 code implementation13 Sep 2021 Yijun Yang, Shujun Wang, Lei Zhu, Lequan Yu

Particularly, for the Extrinsic Consistency, we leverage the knowledge across multiple source domains to enforce data-level consistency.

Data Augmentation Domain Generalization +4

nnFormer: Interleaved Transformer for Volumetric Segmentation

2 code implementations7 Sep 2021 Hong-Yu Zhou, Jiansen Guo, Yinghao Zhang, Lequan Yu, Liansheng Wang, Yizhou Yu

Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community.

Image Segmentation Inductive Bias +3

CateNorm: Categorical Normalization for Robust Medical Image Segmentation

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

We propose a new normalization strategy, named categorical normalization (CateNorm), to normalize the activations according to categorical statistics.

Image Segmentation Medical Image Segmentation +2

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 +3

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

1 code implementation13 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 Image Segmentation +2

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 Image Segmentation +3

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.

Anomaly Detection Domain Generalization +3

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.

Image Segmentation Medical Image Analysis +7

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 Missing Labels +1

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.

General Classification Medical Image Analysis +3

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.

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

11 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 +3

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 +5

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.

Image Segmentation Segmentation +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 +6

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.

Segmentation 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.

Image Segmentation Medical Image Segmentation +3

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 Segmentation +1

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 Segmentation +1

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.

Image Segmentation Medical Image Segmentation +1

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 Image Segmentation +4

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 Segmentation

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