Search Results for author: Huazhu Fu

Found 100 papers, 54 papers with code

Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST

no code implementations bioRxiv 2023 Yahui Long, Kok Siong Ang, Mengwei Li, Kian Long Kelvin Chong, Raman Sethi, Chengwei Zhong, Hang Xu, Zhiwei Ong, Karishma Sachaphibulkij, Ao Chen, Zeng Li, Huazhu Fu, Min Wu, Hsiu Kim Lina Lim, Longqi Liu, Jinmiao Chen

Lastly, compared to other methods, GraphST’s cell type deconvolution achieved higher accuracy on simulated data and better captured spatial niches such as the germinal centers of the lymph node in experimentally acquired data.

Contrastive Learning

Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images

1 code implementation1 Dec 2022 Meng Wang, Kai Yu, Chun-Mei Feng, Ke Zou, Yanyu Xu, Qingquan Meng, Rick Siow Mong Goh, Yong liu, Xinxing Xu, Huazhu Fu

Specifically, aiming at improving the model's ability to learn the complex pathological features of retinal edema lesions in OCT images, we develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module of our newly designed.

Degradation-invariant Enhancement of Fundus Images via Pyramid Constraint Network

1 code implementation18 Oct 2022 Haofeng Liu, Heng Li, Huazhu Fu, Ruoxiu Xiao, Yunshu Gao, Yan Hu, Jiang Liu

For boosting the clinical deployment of fundus image enhancement, this paper proposes the pyramid constraint to develop a degradation-invariant enhancement network (PCE-Net), which mitigates the demand for clinical data and stably enhances unknown data.

Image Enhancement

Learning to screen Glaucoma like the ophthalmologists

no code implementations23 Sep 2022 Junde Wu, Huihui Fang, Fei Li, Huazhu Fu, Yanwu Xu

GAMMA Challenge is organized to encourage the AI models to screen the glaucoma from a combination of 2D fundus image and 3D optical coherence tomography volume, like the ophthalmologists.

Unsupervised Domain Adaptation via Style-Aware Self-intermediate Domain

no code implementations5 Sep 2022 Lianyu Wang, Meng Wang, Daoqiang Zhang, Huazhu Fu

Specifically, we propose a novel learning strategy of SSID, which selects samples from both source and target domains as anchors, and then randomly fuses the object and style features of these anchors to generate labeled and style-rich intermediate auxiliary features for knowledge transfer.

Unsupervised Domain Adaptation

Dataset and Evaluation algorithm design for GOALS Challenge

no code implementations29 Jul 2022 Huihui Fang, Fei Li, Huazhu Fu, Junde Wu, Xiulan Zhang, Yanwu Xu

Glaucoma causes irreversible vision loss due to damage to the optic nerve, and there is no cure for glaucoma. OCT imaging modality is an essential technique for assessing glaucomatous damage since it aids in quantifying fundus structures.

A New Dataset and A Baseline Model for Breast Lesion Detection in Ultrasound Videos

1 code implementation1 Jul 2022 Zhi Lin, Junhao Lin, Lei Zhu, Huazhu Fu, Jing Qin, Liansheng Wang

Moreover, we learn video-level features to classify the breast lesions of the original video as benign or malignant lesions to further enhance the final breast lesion detection performance in ultrasound videos.

Lesion Classification Lesion Detection

TBraTS: Trusted Brain Tumor Segmentation

3 code implementations19 Jun 2022 Ke Zou, Xuedong Yuan, Xiaojing Shen, Meng Wang, Huazhu Fu

In our method, uncertainty is modeled explicitly using subjective logic theory, which treats the predictions of backbone neural network as subjective opinions by parameterizing the class probabilities of the segmentation as a Dirichlet distribution.

Brain Tumor Segmentation Tumor Segmentation

Structure-consistent Restoration Network for Cataract Fundus Image Enhancement

1 code implementation9 Jun 2022 Heng Li, Haofeng Liu, Huazhu Fu, Hai Shu, Yitian Zhao, Xiaoling Luo, Yan Hu, Jiang Liu

In this paper, to circumvent the strict deployment requirement, a structure-consistent restoration network (SCR-Net) for cataract fundus images is developed from synthesized data that shares an identical structure.

Image Enhancement

Progressive Multi-scale Consistent Network for Multi-class Fundus Lesion Segmentation

1 code implementation31 May 2022 Along He, Kai Wang, Tao Li, Wang Bo, Hong Kang, Huazhu Fu

The two proposed PFF and DAB blocks can be integrated with the off-the-shelf backbone networks to address the two issues of multi-scale and feature inconsistency in the multi-class segmentation of fundus lesions, which will produce better feature representation in the feature space.

Lesion Segmentation Semantic Segmentation

GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector

1 code implementation30 May 2022 Peng Zheng, Huazhu Fu, Deng-Ping Fan, Qi Fan, Jie Qin, Yu-Wing Tai, Chi-Keung Tang, Luc van Gool

In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in natural scenes.

Co-Salient Object Detection object-detection +1

Trusted Multi-View Classification with Dynamic Evidential Fusion

1 code implementation25 Apr 2022 Zongbo Han, Changqing Zhang, Huazhu Fu, Joey Tianyi Zhou

With this in mind, we propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC), providing a new paradigm for multi-view learning by dynamically integrating different views at an evidence level.

Classification MULTI-VIEW LEARNING

Global-and-Local Collaborative Learning for Co-Salient Object Detection

no code implementations19 Apr 2022 Runmin Cong, Ning Yang, Chongyi Li, Huazhu Fu, Yao Zhao, Qingming Huang, Sam Kwong

In this paper, we propose a global-and-local collaborative learning architecture, which includes a global correspondence modeling (GCM) and a local correspondence modeling (LCM) to capture comprehensive inter-image corresponding relationship among different images from the global and local perspectives.

Co-Salient Object Detection object-detection +1

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

FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction

1 code implementation CVPR 2022 Liang Gao, Huazhu Fu, Li Li, YingWen Chen, Ming Xu, Cheng-Zhong Xu

Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing their private data.

Federated Learning Image Classification

An Annotation-free Restoration Network for Cataractous Fundus Images

1 code implementation15 Mar 2022 Heng Li, Haofeng Liu, Yan Hu, Huazhu Fu, Yitian Zhao, Hanpei Miao, Jiang Liu

The restoration model is learned from the synthesized images and adapted to real cataract images.

ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images

no code implementations16 Feb 2022 Huihui Fang, Fei Li, Huazhu Fu, Xu sun, Xingxing Cao, Fengbin Lin, Jaemin Son, Sunho Kim, Gwenole Quellec, Sarah Matta, Sharath M Shankaranarayana, Yi-Ting Chen, Chuen-heng Wang, Nisarg A. Shah, Chia-Yen Lee, Chih-Chung Hsu, Hai Xie, Baiying Lei, Ujjwal Baid, Shubham Innani, Kang Dang, Wenxiu Shi, Ravi Kamble, Nitin Singhal, Ching-Wei Wang, Shih-Chang Lo, José Ignacio Orlando, Hrvoje Bogunović, Xiulan Zhang, Yanwu Xu, iChallenge-AMD study group

The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions.

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.

Consistency and Diversity induced Human Motion Segmentation

no code implementations10 Feb 2022 Tao Zhou, Huazhu Fu, Chen Gong, Ling Shao, Fatih Porikli, Haibin Ling, Jianbing Shen

Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion (HSIC) is introduced to ensure the diversity of multi-level subspace representations, which enables the complementarity of multi-level representations to be explored to boost the transfer learning performance.

Motion Segmentation Transfer Learning

Transformers in Medical Imaging: A Survey

1 code implementation24 Jan 2022 Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat, Fahad Shahbaz Khan, Huazhu Fu

Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as {de facto} operators.

Image Classification Image Segmentation +6

Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images

1 code implementation1 Jan 2022 Xiaoqiang Wang, Lei Zhu, Siliang Tang, Huazhu Fu, Ping Li, Fei Wu, Yi Yang, Yueting Zhuang

The depth estimation branch is trained with RGB-D images and then used to estimate the pseudo depth maps for all unlabeled RGB images to form the paired data.

Depth Estimation object-detection +3

Specificity-Preserving Federated Learning for MR Image Reconstruction

1 code implementation9 Dec 2021 Chun-Mei Feng, Yunlu Yan, Shanshan Wang, Yong Xu, Ling Shao, Huazhu Fu

The core idea is to divide the MR reconstruction model into two parts: a globally shared encoder to obtain a generalized representation at the global level, and a client-specific decoder to preserve the domain-specific properties of each client, which is important for collaborative reconstruction when the clients have unique distribution.

Federated Learning Image Reconstruction +1

Trustworthy Long-Tailed Classification

1 code implementation CVPR 2022 Bolian Li, Zongbo Han, Haining Li, Huazhu Fu, Changqing Zhang

To address these issues, we propose a Trustworthy Long-tailed Classification (TLC) method to jointly conduct classification and uncertainty estimation to identify hard samples in a multi-expert framework.

Classification OOD Detection

Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions

1 code implementation NeurIPS 2021 Huan Ma, Zongbo Han, Changqing Zhang, Huazhu Fu, Joey Tianyi Zhou, QinGhua Hu

Multimodal regression is a fundamental task, which integrates the information from different sources to improve the performance of follow-up applications.

Multimodal Sentiment Analysis regression

Deep multi-modal aggregation network for MR image reconstruction with auxiliary modality

2 code implementations15 Oct 2021 Chun-Mei Feng, Huazhu Fu, Tianfei Zhou, Yong Xu, Ling Shao, David Zhang

Magnetic resonance (MR) imaging produces detailed images of organs and tissues with better contrast, but it suffers from a long acquisition time, which makes the image quality vulnerable to say motion artifacts.

Image Reconstruction

Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images

no code implementations5 Oct 2021 Kang Zhou, Jing Li, Weixin Luo, Zhengxin Li, Jianlong Yang, Huazhu Fu, Jun Cheng, Jiang Liu, Shenghua Gao

To mitigate this problem, in this paper, we propose a novel Proxy-bridged Image Reconstruction Network (ProxyAno) for anomaly detection in medical images.

Anomaly Detection Image Reconstruction

Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution

1 code implementation3 Sep 2021 Chun-Mei Feng, Yunlu Yan, Kai Yu, Yong Xu, Ling Shao, Huazhu Fu

Our SANet could explore the areas of high-intensity and low-intensity regions in the "forward" and "reverse" directions with the help of the auxiliary contrast, while learning clearer anatomical structure and edge information for the SR of a target-contrast MR image.

Image Super-Resolution

Specificity-preserving RGB-D Saliency Detection

3 code implementations ICCV 2021 Tao Zhou, Deng-Ping Fan, Geng Chen, Yi Zhou, Huazhu Fu

To effectively fuse cross-modal features in the shared learning network, we propose a cross-enhanced integration module (CIM) and then propagate the fused feature to the next layer for integrating cross-level information.

object-detection Object Detection +3

Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

2 code implementations16 Aug 2021 Bo Dong, Wenhai Wang, Deng-Ping Fan, Jinpeng Li, Huazhu Fu, Ling Shao

Most polyp segmentation methods use CNNs as their backbone, leading to two key issues when exchanging information between the encoder and decoder: 1) taking into account the differences in contribution between different-level features; and 2) designing an effective mechanism for fusing these features.

Medical Image Segmentation

From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data

1 code implementation6 Aug 2021 Ye Liu, Lei Zhu, Shunda Pei, Huazhu Fu, Jing Qin, Qing Zhang, Liang Wan, Wei Feng

Our DID-Net predicts the three component maps by progressively integrating features across scales, and refines each map by passing an independent refinement network.

Image Dehazing Single Image Dehazing

Few-Shot Domain Adaptation with Polymorphic Transformers

1 code implementation10 Jul 2021 Shaohua Li, Xiuchao Sui, Jie Fu, Huazhu Fu, Xiangde Luo, Yangqin Feng, Xinxing Xu, Yong liu, Daniel Ting, Rick Siow Mong Goh

Thus, the chance of overfitting the annotations is greatly reduced, and the model can perform robustly on the target domain after being trained on a few annotated images.

Domain Adaptation

Multi-Modal Transformer for Accelerated MR Imaging

1 code implementation27 Jun 2021 Chun-Mei Feng, Yunlu Yan, Geng Chen, Yong Xu, Ling Shao, Huazhu Fu

To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging.

Image Reconstruction Super-Resolution

Federated Noisy Client Learning

1 code implementation24 Jun 2021 Kahou Tam, Li Li, Bo Han, Chengzhong Xu, Huazhu Fu

Federated learning (FL) collaboratively trains a shared global model depending on multiple local clients, while keeping the training data decentralized in order to preserve data privacy.

Federated Learning

Task Transformer Network for Joint MRI Reconstruction and Super-Resolution

1 code implementation12 Jun 2021 Chun-Mei Feng, Yunlu Yan, Huazhu Fu, Li Chen, Yong Xu

Then, a task transformer module is designed to embed and synthesize the relevance between the two tasks.

MRI Reconstruction Super-Resolution

A Multi-Branch Hybrid Transformer Networkfor Corneal Endothelial Cell Segmentation

no code implementations21 May 2021 Yinglin Zhang, Risa Higashita, Huazhu Fu, Yanwu Xu, Yang Zhang, Haofeng Liu, Jian Zhang, Jiang Liu

Corneal endothelial cell segmentation plays a vital role inquantifying clinical indicators such as cell density, coefficient of variation, and hexagonality.

Cell Segmentation

Combating Ambiguity for Hash-code Learning in Medical Instance Retrieval

1 code implementation19 May 2021 Jiansheng Fang, Huazhu Fu, Dan Zeng, Xiao Yan, Yuguang Yan, Jiang Liu

When encountering a dubious diagnostic case, medical instance retrieval can help radiologists make evidence-based diagnoses by finding images containing instances similar to a query case from a large image database.

Retrieval Specificity

Multi-Contrast MRI Super-Resolution via a Multi-Stage Integration Network

1 code implementation19 May 2021 Chun-Mei Feng, Huazhu Fu, Shuhao Yuan, Yong Xu

In this work, we propose a multi-stage integration network (i. e., MINet) for multi-contrast MRI SR, which explicitly models the dependencies between multi-contrast images at different stages to guide image SR.

Super-Resolution

DONet: Dual-Octave Network for Fast MR Image Reconstruction

no code implementations12 May 2021 Chun-Mei Feng, Zhanyuan Yang, Huazhu Fu, Yong Xu, Jian Yang, Ling Shao

In this paper, we propose the Dual-Octave Network (DONet), which is capable of learning multi-scale spatial-frequency features from both the real and imaginary components of MR data, for fast parallel MR image reconstruction.

Image Reconstruction

VideoLT: Large-scale Long-tailed Video Recognition

1 code implementation ICCV 2021 Xing Zhang, Zuxuan Wu, Zejia Weng, Huazhu Fu, Jingjing Chen, Yu-Gang Jiang, Larry Davis

In this paper, we introduce VideoLT, a large-scale long-tailed video recognition dataset, as a step toward real-world video recognition.

Image Classification Video Recognition

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

Group Collaborative Learning for Co-Salient Object Detection

1 code implementation CVPR 2021 Qi Fan, Deng-Ping Fan, Huazhu Fu, Chi Keung Tang, Ling Shao, Yu-Wing Tai

We present a novel group collaborative learning framework (GCoNet) capable of detecting co-salient objects in real time (16ms), by simultaneously mining consensus representations at group level based on the two necessary criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects by capturing their inherent shared attributes using our novel group affinity module; 2) inter-group separability to effectively suppress the influence of noisy objects on the output by introducing our new group collaborating module conditioning the inconsistent consensus.

Co-Salient Object Detection object-detection +1

Triple-cooperative Video Shadow Detection

1 code implementation CVPR 2021 Zhihao Chen, Liang Wan, Lei Zhu, Jia Shen, Huazhu Fu, Wennan Liu, Jing Qin

The bottleneck is the lack of a well-established dataset with high-quality annotations for video shadow detection.

Saliency Detection Semantic Segmentation +3

Trusted Multi-View Classification

4 code implementations ICLR 2021 Zongbo Han, Changqing Zhang, Huazhu Fu, Joey Tianyi Zhou

To this end, we propose a novel multi-view classification method, termed trusted multi-view classification, which provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level.

Classification General Classification +1

Deep Triplet Hashing Network for Case-based Medical Image Retrieval

1 code implementation29 Jan 2021 Jiansheng Fang, Huazhu Fu, Jiang Liu

The triplet cross-entropy loss can help to map the classification information of images and similarity between images into the hash codes.

Classification General Classification +2

Applications of Deep Learning in Fundus Images: A Review

1 code implementation25 Jan 2021 Tao Li, Wang Bo, Chunyu Hu, Hong Kang, Hanruo Liu, Kai Wang, Huazhu Fu

The use of fundus images for the early screening of eye diseases is of great clinical importance.

Image Generation Lesion Segmentation

Multi-View Disentangled Representation

no code implementations1 Jan 2021 Zongbo Han, Changqing Zhang, Huazhu Fu, QinGhua Hu, Joey Tianyi Zhou

Learning effective representations for data with multiple views is crucial in machine learning and pattern recognition.

Disentanglement

Visual-Textual Attentive Semantic Consistency for Medical Report Generation

no code implementations ICCV 2021 Yi Zhou, Lei Huang, Tao Zhou, Huazhu Fu, Ling Shao

Second, the progressive report decoder consists of a sentence decoder and a word decoder, where we propose image-sentence matching and description accuracy losses to constrain the visual-textual semantic consistency.

Medical Report Generation Word Embeddings

Colonoscopy Polyp Detection: Domain Adaptation From Medical Report Images to Real-time Videos

no code implementations31 Dec 2020 Zhi-Qin Zhan, Huazhu Fu, Yan-Yao Yang, Jingjing Chen, Jie Liu, Yu-Gang Jiang

However, there are several issues between the image-based training and video-based inference, including domain differences, lack of positive samples, and temporal smoothness.

Domain Adaptation

Deep Partial Multi-View Learning

no code implementations12 Nov 2020 Changqing Zhang, Yajie Cui, Zongbo Han, Joey Tianyi Zhou, Huazhu Fu, QinGhua Hu

Although multi-view learning has made signifificant progress over the past few decades, it is still challenging due to the diffificulty in modeling complex correlations among different views, especially under the context of view missing.

Imputation MULTI-VIEW LEARNING +1

CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging

1 code implementation15 Oct 2020 Lei Mou, Yitian Zhao, Huazhu Fu, Yonghuai Liu, Jun Cheng, Yalin Zheng, Pan Su, Jianlong Yang, Li Chen, Alejandro F Frang, Masahiro Akiba, Jiang Liu

Automated detection of curvilinear structures, e. g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in automatic image interpretation associated to the management of many diseases.

Management

M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging

no code implementations7 Oct 2020 Xuelin Qian, Huazhu Fu, Weiya Shi, Tao Chen, Yanwei Fu, Fei Shan, xiangyang xue

To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic.

Adversarial Exposure Attack on Diabetic Retinopathy Imagery

no code implementations19 Sep 2020 Yupeng Cheng, Felix Juefei-Xu, Qing Guo, Huazhu Fu, Xiaofei Xie, Shang-Wei Lin, Weisi Lin, Yang Liu

In this paper, we study this problem from the viewpoint of adversarial attack and identify a totally new task, i. e., adversarial exposure attack generating adversarial images by tuning image exposure to mislead the DNNs with significantly high transferability.

Adversarial Attack

NuI-Go: Recursive Non-Local Encoder-Decoder Network for Retinal Image Non-Uniform Illumination Removal

no code implementations7 Aug 2020 Chongyi Li, Huazhu Fu, Runmin Cong, Zechao Li, Qianqian Xu

We further demonstrate the advantages of the proposed method for improving the accuracy of retinal vessel segmentation.

Retinal Vessel Segmentation

ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model

1 code implementation10 Jul 2020 Yuhui Ma, Huaying Hao, Huazhu Fu, Jiong Zhang, Jianlong Yang, Jiang Liu, Yalin Zheng, Yitian Zhao

To address these issues, for the first time in the field of retinal image analysis we construct a dedicated Retinal OCT-A SEgmentation dataset (ROSE), which consists of 229 OCT-A images with vessel annotations at either centerline-level or pixel level.

Retinal Vessel Segmentation

Re-thinking Co-Salient Object Detection

2 code implementations7 Jul 2020 Deng-Ping Fan, Tengpeng Li, Zheng Lin, Ge-Peng Ji, Dingwen Zhang, Ming-Ming Cheng, Huazhu Fu, Jianbing Shen

CoSOD is an emerging and rapidly growing extension of salient object detection (SOD), which aims to detect the co-occurring salient objects in a group of images.

Co-Salient Object Detection object-detection +1

PraNet: Parallel Reverse Attention Network for Polyp Segmentation

4 code implementations13 Jun 2020 Deng-Ping Fan, Ge-Peng Ji, Tao Zhou, Geng Chen, Huazhu Fu, Jianbing Shen, Ling Shao

To address these challenges, we propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images.

Ranked #4 on Camouflaged Object Segmentation on CAMO (using extra training data)

Camouflaged Object Segmentation Camouflage Segmentation +1

Open-Narrow-Synechiae Anterior Chamber Angle Classification in AS-OCT Sequences

no code implementations9 Jun 2020 Huaying Hao, Huazhu Fu, Yanwu Xu, Jianlong Yang, Fei Li, Xiulan Zhang, Jiang Liu, Yitian Zhao

However, clinical diagnosis requires a more discriminating ACA three-class system (i. e., open, narrow, or synechiae angles) for the benefit of clinicians who seek better to understand the progression of the spectrum of angle-closure glaucoma types.

General Classification

M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients

no code implementations1 Jun 2020 Tao Zhou, Huazhu Fu, Yu Zhang, Changqing Zhang, Xiankai Lu, Jianbing Shen, Ling Shao

Then, we use a modality-specific network to extract implicit and high-level features from different MR scans.

Modeling and Enhancing Low-quality Retinal Fundus Images

1 code implementation12 May 2020 Ziyi Shen, Huazhu Fu, Jianbing Shen, Ling Shao

Retinal fundus images are widely used for the clinical screening and diagnosis of eye diseases.

Image Enhancement Retinal Vessel Segmentation

Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesis

2 code implementations11 Feb 2020 Tao Zhou, Huazhu Fu, Geng Chen, Jianbing Shen, Ling Shao

Medical image synthesis has been proposed as an effective solution to this, where any missing modalities are synthesized from the existing ones.

Image Generation

CPM-Nets: Cross Partial Multi-View Networks

1 code implementation NeurIPS 2019 Changqing Zhang, Zongbo Han, Yajie Cui, Huazhu Fu, Joey Tianyi Zhou, QinGhua Hu

Despite multi-view learning progressed fast in past decades, it is still challenging due to the difficulty in modeling complex correlation among different views, especially under the context of view missing.

MULTI-VIEW LEARNING

Sparse-GAN: Sparsity-constrained Generative Adversarial Network for Anomaly Detection in Retinal OCT Image

no code implementations28 Nov 2019 Kang Zhou, Shenghua Gao, Jun Cheng, Zaiwang Gu, Huazhu Fu, Zhi Tu, Jianlong Yang, Yitian Zhao, Jiang Liu

With the development of convolutional neural network, deep learning has shown its success for retinal disease detection from optical coherence tomography (OCT) images.

Anomaly Detection

Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces

2 code implementations10 Jul 2019 Huazhu Fu, Boyang Wang, Jianbing Shen, Shanshan Cui, Yanwu Xu, Jiang Liu, Ling Shao

Retinal image quality assessment (RIQA) is essential for controlling the quality of retinal imaging and guaranteeing the reliability of diagnoses by ophthalmologists or automated analysis systems.

Image Quality Assessment

Salient Object Detection in the Deep Learning Era: An In-Depth Survey

1 code implementation19 Apr 2019 Wenguan Wang, Qiuxia Lai, Huazhu Fu, Jianbing Shen, Haibin Ling, Ruigang Yang

As an essential problem in computer vision, salient object detection (SOD) has attracted an increasing amount of research attention over the years.

object-detection RGB Salient Object Detection +2

CE-Net: Context Encoder Network for 2D Medical Image Segmentation

3 code implementations7 Mar 2019 Zaiwang Gu, Jun Cheng, Huazhu Fu, Kang Zhou, Huaying Hao, Yitian Zhao, Tianyang Zhang, Shenghua Gao, Jiang Liu

In this paper, we propose a context encoder network (referred to as CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation.

Cell Segmentation Image Segmentation +2

Angle-Closure Detection in Anterior Segment OCT based on Multi-Level Deep Network

no code implementations10 Feb 2019 Huazhu Fu, Yanwu Xu, Stephen Lin, Damon Wing Kee Wong, Mani Baskaran, Meenakshi Mahesh, Tin Aung, Jiang Liu

A Multi-Level Deep Network (MLDN) is proposed to formulate this learning, which utilizes three particular AS-OCT regions based on clinical priors: the global anterior segment structure, local iris region, and anterior chamber angle (ACA) patch.

HSCS: Hierarchical Sparsity Based Co-saliency Detection for RGBD Images

no code implementations16 Nov 2018 Runmin Cong, Jianjun Lei, Huazhu Fu, Qingming Huang, Xiaochun Cao, Nam Ling

In this paper, we propose a novel co-saliency detection method for RGBD images based on hierarchical sparsity reconstruction and energy function refinement.

Co-Salient Object Detection

Multi-Context Deep Network for Angle-Closure Glaucoma Screening in Anterior Segment OCT

no code implementations10 Sep 2018 Huazhu Fu, Yanwu Xu, Stephen Lin, Damon Wing Kee Wong, Baskaran Mani, Meenakshi Mahesh, Tin Aung, Jiang Liu

A major cause of irreversible visual impairment is angle-closure glaucoma, which can be screened through imagery from Anterior Segment Optical Coherence Tomography (AS-OCT).

General Classification

Person Re-Identification by Semantic Region Representation and Topology Constraint

no code implementations20 Aug 2018 Jianjun Lei, Lijie Niu, Huazhu Fu, Bo Peng, Qingming Huang, Chunping Hou

In this paper, we propose a novel person re-identification method, which consists of a reliable representation called Semantic Region Representation (SRR), and an effective metric learning with Mapping Space Topology Constraint (MSTC).

Metric Learning Person Re-Identification

Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image

3 code implementations19 May 2018 Huazhu Fu, Jun Cheng, Yanwu Xu, Changqing Zhang, Damon Wing Kee Wong, Jiang Liu, Xiaochun Cao

Specifically, a novel Disc-aware Ensemble Network (DENet) for automatic glaucoma screening is proposed, which integrates the deep hierarchical context of the global fundus image and the local optic disc region.

A Cascaded Convolutional Neural Network for Single Image Dehazing

no code implementations21 Mar 2018 Chongyi Li, Jichang Guo, Fatih Porikli, Huazhu Fu, Yanwei Pang

Different from previous learning-based methods, we propose a flexible cascaded CNN for single hazy image restoration, which considers the medium transmission and global atmospheric light jointly by two task-driven subnetworks.

Image Dehazing Image Restoration +1

Review of Visual Saliency Detection with Comprehensive Information

no code implementations9 Mar 2018 Runmin Cong, Jianjun Lei, Huazhu Fu, Ming-Ming Cheng, Weisi Lin, Qingming Huang

With the acquisition technology development, more comprehensive information, such as depth cue, inter-image correspondence, or temporal relationship, is available to extend image saliency detection to RGBD saliency detection, co-saliency detection, or video saliency detection.

Co-Salient Object Detection Video Saliency Detection

Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation

3 code implementations3 Jan 2018 Huazhu Fu, Jun Cheng, Yanwu Xu, Damon Wing Kee Wong, Jiang Liu, Xiaochun Cao

The proposed M-Net mainly consists of multi-scale input layer, U-shape convolutional network, side-output layer, and multi-label loss function.

An Iterative Co-Saliency Framework for RGBD Images

no code implementations4 Nov 2017 Runmin Cong, Jianjun Lei, Huazhu Fu, Weisi Lin, Qingming Huang, Xiaochun Cao, Chunping Hou

In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD cosaliency map by using a refinement-cycle model.

Co-Salient Object Detection

Co-saliency Detection for RGBD Images Based on Multi-constraint Feature Matching and Cross Label Propagation

no code implementations14 Oct 2017 Runmin Cong, Jianjun Lei, Huazhu Fu, Qingming Huang, Xiaochun Cao, Chunping Hou

Different from the most existing co-saliency methods focusing on RGB images, this paper proposes a novel co-saliency detection model for RGBD images, which utilizes the depth information to enhance identification of co-saliency.

Co-Salient Object Detection

Latent Multi-View Subspace Clustering

no code implementations CVPR 2017 Changqing Zhang, QinGhua Hu, Huazhu Fu, Pengfei Zhu, Xiaochun Cao

In this paper, we propose a novel Latent Multi-view Subspace Clustering (LMSC) method, which clusters data points with latent representation and simultaneously explores underlying complementary information from multiple views.

Multi-view Subspace Clustering

Low-Rank Tensor Constrained Multiview Subspace Clustering

no code implementations ICCV 2015 Changqing Zhang, Huazhu Fu, Si Liu, Guangcan Liu, Xiaochun Cao

We introduce a low-rank tensor constraint to explore the complementary information from multiple views and, accordingly, establish a novel method called Low-rank Tensor constrained Multiview Subspace Clustering (LT-MSC).

Diversity-Induced Multi-View Subspace Clustering

no code implementations CVPR 2015 Xiaochun Cao, Changqing Zhang, Huazhu Fu, Si Liu, Hua Zhang

In this paper, we focus on how to boost the multi-view clustering by exploring the complementary information among multi-view features.

Face Clustering Multi-view Subspace Clustering

Object-Based RGBD Image Co-Segmentation With Mutex Constraint

no code implementations CVPR 2015 Huazhu Fu, Dong Xu, Stephen Lin, Jiang Liu

We present an object-based co-segmentation method that takes advantage of depth data and is able to correctly handle noisy images in which the common foreground object is missing.

Object-based Multiple Foreground Video Co-segmentation

no code implementations CVPR 2014 Huazhu Fu, Dong Xu, Bao Zhang, Stephen Lin

We present a video co-segmentation method that uses category-independent object proposals as its basic element and can extract multiple foreground objects in a video set.

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