Search Results for author: Xiaoying Tang

Found 50 papers, 24 papers with code

Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexing

no code implementations25 May 2024 Yongxin Guo, Lin Wang, Xiaoying Tang, Tao Lin

To demonstrate the effectiveness of the proposed Client2Vec method, we conduct three case studies that assess the impact of the client index on the FL training process.

Federated Learning Privacy Preserving

Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

1 code implementation23 May 2024 Yongxin Guo, Zhenglin Cheng, Xiaoying Tang, Tao Lin

The Sparse Mixture of Experts (SMoE) has been widely employed to enhance the efficiency of training and inference for Transformer-based foundational models, yielding promising results.

Visual Question Answering

VTG-LLM: Integrating Timestamp Knowledge into Video LLMs for Enhanced Video Temporal Grounding

1 code implementation22 May 2024 Yongxin Guo, Jingyu Liu, Mingda Li, Xiaoying Tang, Xi Chen, Bo Zhao

Video Temporal Grounding (VTG) focuses on accurately identifying event timestamps within a particular video based on a linguistic query, playing a vital role in downstream tasks such as video browsing and editing.

Dense Video Captioning Highlight Detection +2

FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation

no code implementations27 Feb 2024 Li Lin, Yixiang Liu, Jiewei Wu, Pujin Cheng, Zhiyuan Cai, Kenneth K. Y. Wong, Xiaoying Tang

In such context, we propose a novel personalized FL framework with learnable prompt and aggregation (FedLPPA) to uniformly leverage heterogeneous weak supervision for medical image segmentation.

Decoder Federated Learning +5

Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing

no code implementations2 Jan 2024 Zhe Kong, Wentian Zhang, Tao Wang, Kaihao Zhang, Yuexiang Li, Xiaoying Tang, Wenhan Luo

In this paper, we propose a domain adversarial attack (DAA) method to mitigate the training instability problem by adding perturbations to the input images, which makes them indistinguishable across domains and enables domain alignment.

Adversarial Attack Face Anti-Spoofing +2

ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor Segmentation

no code implementations13 Dec 2023 Shiyun Chen, Li Lin, Pujin Cheng, Xiaoying Tang

Recently, Segment Anything Model (SAM) has shown promising performance in some medical image segmentation tasks, but it performs poorly for liver tumor segmentation.

General Knowledge Image Segmentation +3

Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retinal OCT Images with Full and Sparse Annotations

1 code implementation4 Dec 2023 Hong Liu, Dong Wei, Donghuan Lu, Xiaoying Tang, Liansheng Wang, Yefeng Zheng

Experiments on a synthetic dataset and three public clinical datasets show that our framework can effectively align the B-scans for potential motion correction, and achieves superior performance to state-of-the-art 2D deep learning methods in terms of both layer segmentation accuracy and cross-B-scan 3D continuity in both fully and semi-supervised settings, thus offering more clinical values than previous works.


FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems

no code implementations31 Oct 2023 Lin Wang, Zhichao Wang, Xi Leng, Xiaoying Tang

Preserving privacy and reducing communication costs for edge users pose significant challenges in recommendation systems.

Federated Learning Recommendation Systems

Find Your Optimal Assignments On-the-fly: A Holistic Framework for Clustered Federated Learning

no code implementations9 Oct 2023 Yongxin Guo, Xiaoying Tang, Tao Lin

To this end, this paper presents a comprehensive investigation into current clustered FL methods and proposes a four-tier framework, namely HCFL, to encompass and extend existing approaches.

Clustering Federated Learning

Enhanced Federated Optimization: Adaptive Unbiased Sampling with Reduced Variance

no code implementations4 Oct 2023 Dun Zeng, Zenglin Xu, Yu Pan, Xu Luo, Qifan Wang, Xiaoying Tang

Central to this process is the technique of unbiased client sampling, which ensures a representative selection of clients.

Federated Learning

FedAWARE: Maximizing Gradient Diversity for Heterogeneous Federated Server-side Optimization

2 code implementations4 Oct 2023 Dun Zeng, Zenglin Xu, Yu Pan, Qifan Wang, Xiaoying Tang

Furthermore, our results show that \textsc{FedAWARE} can enhance the performance of FL algorithms as a plug-in module.

Federated Learning

PRIOR: Prototype Representation Joint Learning from Medical Images and Reports

1 code implementation ICCV 2023 Pujin Cheng, Li Lin, Junyan Lyu, Yijin Huang, Wenhan Luo, Xiaoying Tang

In this paper, we present a prototype representation learning framework incorporating both global and local alignment between medical images and reports.

Contrastive Learning Image-to-Text Retrieval +8

TriFormer: A Multi-modal Transformer Framework For Mild Cognitive Impairment Conversion Prediction

no code implementations14 Jul 2023 Linfeng Liu, Junyan Lyu, Siyu Liu, Xiaoying Tang, Shekhar S. Chandra, Fatima A. Nasrallah

The prediction of mild cognitive impairment (MCI) conversion to Alzheimer's disease (AD) is important for early treatment to prevent or slow the progression of AD.

JOINEDTrans: Prior Guided Multi-task Transformer for Joint Optic Disc/Cup Segmentation and Fovea Detection

no code implementations19 May 2023 Huaqing He, Li Lin, Zhiyuan Cai, Pujin Cheng, Xiaoying Tang

To address these issues, we propose a prior guided multi-task transformer framework for joint OD/OC segmentation and fovea detection, named JOINEDTrans.

Fovea Detection Image Segmentation +2

MProtoNet: A Case-Based Interpretable Model for Brain Tumor Classification with 3D Multi-parametric Magnetic Resonance Imaging

1 code implementation13 Apr 2023 Yuanyuan Wei, Roger Tam, Xiaoying Tang

Recent applications of deep convolutional neural networks in medical imaging raise concerns about their interpretability.

Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation

1 code implementation12 Apr 2023 Li Lin, Jiewei Wu, Yixiang Liu, Kenneth K. Y. Wong, Xiaoying Tang

The statistical heterogeneity (e. g., non-IID data and domain shifts) is a primary obstacle in FL, impairing the generalization performance of the global model.

Federated Learning Image Segmentation +4

Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement

1 code implementation8 Mar 2023 Puijin Cheng, Li Lin, Yijin Huang, Huaqing He, Wenhan Luo, Xiaoying Tang

In this paper, we introduce a novel diffusion model based framework, named Learning Enhancement from Degradation (LED), for enhancing fundus images.

Image Enhancement

FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering

no code implementations29 Jan 2023 Yongxin Guo, Xiaoying Tang, Tao Lin

In this paper, we identify the learning challenges posed by the simultaneous occurrence of diverse distribution shifts and propose a clustering principle to overcome these challenges.

Clustering Federated Learning

FedEBA+: Towards Fair and Effective Federated Learning via Entropy-Based Model

no code implementations29 Jan 2023 Lin Wang, Zhichao Wang, Sai Praneeth Karimireddy, Xiaoying Tang

Ensuring fairness is a crucial aspect of Federated Learning (FL), which enables the model to perform consistently across all clients.

Fairness Federated Learning

Network analysis on cortical morphometry in first-episode schizophrenia

no code implementations26 Dec 2022 Mowen Yin, Weikai Huang, Zhichao Liang, Quanying Liu, Xiaoying Tang

Our work supports that cortical morphological connectivity, which is constructed based on correlations across subjects' cortical thickness, may serve as a tool to study topological abnormalities in neurological disorders.


YoloCurvSeg: You Only Label One Noisy Skeleton for Vessel-style Curvilinear Structure Segmentation

1 code implementation11 Dec 2022 Li Lin, Linkai Peng, Huaqing He, Pujin Cheng, Jiewei Wu, Kenneth K. Y. Wong, Xiaoying Tang

With only one noisy skeleton annotation (respectively 0. 14\%, 0. 03\%, 1. 40\%, and 0. 65\% of the full annotation), YoloCurvSeg achieves more than 97\% of the fully-supervised performance on each dataset.

Contrastive Learning Image Generation +4

SSiT: Saliency-guided Self-supervised Image Transformer for Diabetic Retinopathy Grading

1 code implementation20 Oct 2022 Yijin Huang, Junyan Lyu, Pujin Cheng, Roger Tam, Xiaoying Tang

Specifically, two saliency-guided learning tasks are employed in SSiT: (1) Saliency-guided contrastive learning is conducted based on the momentum contrast, wherein fundus images' saliency maps are utilized to remove trivial patches from the input sequences of the momentum-updated key encoder.

Contrastive Learning Diabetic Retinopathy Grading +1

Diversity Boosted Learning for Domain Generalization with Large Number of Domains

no code implementations28 Jul 2022 Xi Leng, Xiaoying Tang, Yatao Bian

Machine learning algorithms minimizing the average training loss usually suffer from poor generalization performance due to the greedy exploitation of correlations among the training data, which are not stable under distributional shifts.

Domain Generalization Point Processes +1

AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation

1 code implementation27 Jul 2022 Junyan Lyu, Yiqi Zhang, Yijin Huang, Li Lin, Pujin Cheng, Xiaoying Tang

To address this issue, we propose a data manipulation based domain generalization method, called Automated Augmentation for Domain Generalization (AADG).

Data Augmentation Domain Generalization +5

FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction

1 code implementation26 May 2022 Yongxin Guo, Xiaoying Tang, Tao Lin

As a remedy, we propose FedBR, a novel unified algorithm that reduces the local learning bias on features and classifiers to tackle these challenges.

Domain Generalization Federated Learning

DS3-Net: Difficulty-perceived Common-to-T1ce Semi-Supervised Multimodal MRI Synthesis Network

no code implementations14 Mar 2022 Ziqi Huang, Li Lin, Pujin Cheng, Kai Pan, Xiaoying Tang

Furthermore, with only 5% paired data, the proposed DS3-Net achieves competitive performance with state-of-theart image translation methods utilizing 100% paired data, delivering an average SSIM of 0. 8947 and an average PSNR of 23. 60.

Knowledge Distillation SSIM +1

LesionPaste: One-Shot Anomaly Detection for Medical Images

no code implementations12 Mar 2022 Weikai Huang, Yijin Huang, Xiaoying Tang

Then, MixUp is adopted to paste patches from the lesion bank at random positions in normal images to synthesize anomalous samples for training.

Semi-supervised Anomaly Detection Supervised Anomaly Detection

Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion

no code implementations9 Mar 2022 Ziqi Huang, Li Lin, Pujin Cheng, Linkai Peng, Xiaoying Tang

As such, it is clinically meaningful to develop a method to synthesize unavailable modalities which can also be used as additional inputs to downstream tasks (e. g., brain tumor segmentation) for performance enhancing.

Brain Tumor Segmentation Contrastive Learning +2

Uni4Eye: Unified 2D and 3D Self-supervised Pre-training via Masked Image Modeling Transformer for Ophthalmic Image Classification

no code implementations9 Mar 2022 Zhiyuan Cai, Li Lin, Huaqing He, Xiaoying Tang

We employ a Unified Patch Embedding module to replace the origin patch embedding module in ViT for jointly processing both 2D and 3D input images.

Image Classification Self-Supervised Learning

JOINED : Prior Guided Multi-task Learning for Joint Optic Disc/Cup Segmentation and Fovea Detection

1 code implementation1 Mar 2022 Huaqing He, Li Lin, Zhiyuan Cai, Xiaoying Tang

At the coarse stage, we obtain the OD/OC coarse segmentation and the heatmap localization of fovea through a joint segmentation and detection module.

Fovea Detection Multi-Task Learning +1

A Holistic Review on Advanced Bi-directional EV Charging Control Algorithms

no code implementations28 Feb 2022 Xiaoying Tang, Chenxi Sun, Suzhi Bi, Shuoyao Wang, Angela Yingjun Zhang

The rapid growth of electric vehicles (EVs) has promised a next-generation transportation system with reduced carbon emission.

energy trading Scheduling

GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges

no code implementations14 Feb 2022 Junde Wu, Huihui Fang, Fei Li, Huazhu Fu, Fengbin Lin, Jiongcheng Li, Lexing Huang, Qinji Yu, Sifan Song, Xinxing Xu, Yanyu Xu, Wensai Wang, Lingxiao Wang, Shuai Lu, Huiqi Li, Shihua Huang, Zhichao Lu, Chubin Ou, Xifei Wei, Bingyuan Liu, Riadh Kobbi, Xiaoying Tang, Li Lin, Qiang Zhou, Qiang Hu, Hrvoje Bogunovic, José Ignacio Orlando, Xiulan Zhang, Yanwu Xu

However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.

Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning

1 code implementation13 Jan 2022 Linkai Peng, Li Lin, Pujin Cheng, Ziqi Huang, Xiaoying Tang

The two models use labeled data (together with the corresponding transferred images) for supervised learning and perform collaborative consistency learning on unlabeled data.

Image Reconstruction Retinal Vessel Segmentation +3

COROLLA: An Efficient Multi-Modality Fusion Framework with Supervised Contrastive Learning for Glaucoma Grading

1 code implementation11 Jan 2022 Zhiyuan Cai, Li Lin, Huaqing He, Xiaoying Tang

In this paper, we propose an efficient multi-modality supervised contrastive learning framework, named COROLLA, for glaucoma grading.

Contrastive Learning

Towards Federated Learning on Time-Evolving Heterogeneous Data

no code implementations25 Dec 2021 Yongxin Guo, Tao Lin, Xiaoying Tang

Federated Learning (FL) is a learning paradigm that protects privacy by keeping client data on edge devices.

Federated Learning

Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation

2 code implementations27 Oct 2021 Yijin Huang, Li Lin, Pujin Cheng, Junyan Lyu, Roger Tam, Xiaoying Tang

To identify the key components in a standard deep learning framework (ResNet-50) for DR grading, we systematically analyze the impact of several major components.

Data Augmentation Diabetic Retinopathy Grading

LDDMM-Face: Large Deformation Diffeomorphic Metric Learning for Cross-annotation Face Alignment

1 code implementation29 Sep 2021 Huilin Yang, Junyan Lyu, Pujin Cheng, Roger Tam, Xiaoying Tang

We innovatively propose a flexible and consistent cross-annotation face alignment framework, LDDMM-Face, the key contribution of which is a deformation layer that naturally embeds facial geometry in a diffeomorphic way.

Face Alignment Metric Learning

LDDMM-Face: Large Deformation Diffeomorphic Metric Learning for Flexible and Consistent Face Alignment

no code implementations2 Aug 2021 Huilin Yang, Junyan Lyu, Pujin Cheng, Xiaoying Tang

Instead of predicting facial landmarks via heatmap or coordinate regression, we formulate this task in a diffeomorphic registration manner and predict momenta that uniquely parameterize the deformation between initial boundary and true boundary, and then perform large deformation diffeomorphic metric mapping (LDDMM) simultaneously for curve and landmark to localize the facial landmarks.

Face Alignment Metric Learning +1

Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images

2 code implementations17 Jul 2021 Yijin Huang, Li Lin, Pujin Cheng, Junyan Lyu, Xiaoying Tang

Instead of taking entire images as the input in the common contrastive learning scheme, lesion patches are employed to encourage the feature extractor to learn representations that are highly discriminative for DR grading.

Contrastive Learning Data Augmentation +2

BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images

1 code implementation10 Jul 2021 Li Lin, Zhonghua Wang, Jiewei Wu, Yijin Huang, Junyan Lyu, Pujin Cheng, Jiong Wu, Xiaoying Tang

Moreover, both low-level and high-level features from the aforementioned three branches, including shape, size, boundary, and signed directional distance map of FAZ, are fused hierarchically with features from the diagnostic classifier.

Classification Segmentation

DRR4Covid: Learning Automated COVID-19 Infection Segmentation from Digitally Reconstructed Radiographs

1 code implementation26 Aug 2020 Pengyi Zhang, Yunxin Zhong, Yulin Deng, Xiaoying Tang, Xiaoqiong Li

The infection-aware DRR generator is able to produce DRRs with adjustable strength of radiological signs of COVID-19 infection, and generate pixel-level infection annotations that match the DRRs precisely.

COVID-19 Diagnosis Domain Adaptation +1

Learning Diagnosis of COVID-19 from a Single Radiological Image

1 code implementation arXiv:2006.12220 2020 Pengyi Zhang, Yunxin Zhong, Xiaoying Tang, Yunlin Deng, Xiaoqiong Li

To address this problem, we explore the feasibility of learning deep models for COVID-19 diagnosis from a single radiological image by resorting to synthesizing diverse radiological images.

COVID-19 Diagnosis Data Augmentation

A Survey on Deep Learning of Small Sample in Biomedical Image Analysis

1 code implementation1 Aug 2019 Pengyi Zhang, Yunxin Zhong, Yulin Deng, Xiaoying Tang, Xiaoqiong Li

In order to accelerate the clinical usage of biomedical image analysis based on deep learning techniques, we intentionally expand this survey to include the explanation methods for deep models that are important to clinical decision making.

Active Learning Data Augmentation +4

Brain segmentation based on multi-atlas guided 3D fully convolutional network ensembles

no code implementations5 Jan 2019 Jiong Wu, Xiaoying Tang

To address this limitation, we trained a 3D FCN model for each ROI using patches of adaptive size and embedded outputs of the convolutional layers in the deconvolutional layers to further capture the local and global context patterns.

Brain Segmentation Segmentation

Prostate Segmentation using 2D Bridged U-net

no code implementations12 Jul 2018 Wanli Chen, Yue Zhang, Junjun He, Yu Qiao, Yi-fan Chen, Hongjian Shi, Xiaoying Tang

To address the aforementioned three problems, we propose and validate a deeper network that can fit medical image datasets that are usually small in the sample size.

Image Segmentation Medical Image Segmentation +2

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