Search Results for author: Jiang Liu

Found 55 papers, 21 papers with code

PPCR: Learning Pyramid Pixel Context Recalibration Module for Medical Image Classification

no code implementations3 Mar 2023 Xiaoqing Zhang, Zunjie Xiao, Xiao Wu, Jiansheng Fang, Junyong Shen, Yan Hu, Risa Higashita, Jiang Liu

Spatial attention mechanism has been widely incorporated into deep convolutional neural networks (CNNs) via long-range dependency capturing, significantly lifting the performance in computer vision, but it may perform poorly in medical imaging.

Decision Making Image Classification +1

PolyFormer: Referring Image Segmentation as Sequential Polygon Generation

no code implementations14 Feb 2023 Jiang Liu, Hui Ding, Zhaowei Cai, Yuting Zhang, Ravi Kumar Satzoda, Vijay Mahadevan, R. Manmatha

In this work, instead of directly predicting the pixel-level segmentation masks, the problem of referring image segmentation is formulated as sequential polygon generation, and the predicted polygons can be later converted into segmentation masks.

 Ranked #1 on Referring Expression Segmentation on ReferIt (using extra training data)

Image Segmentation Quantization +5

Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module

no code implementations17 Nov 2022 Jiayi Zhang, Xiaoshan Chen, Zhongxi Qiu, Mingming Yang, Yan Hu, Jiang Liu

Specifically, we propose a fusion module named Multi-scale Attention Fusion (MAF) module for our dual-stream framework to effectively integrate features of the two tasks.

Boundary Detection Super-Resolution

TOE: A Grid-Tagging Discontinuous NER Model Enhanced by Embedding Tag/Word Relations and More Fine-Grained Tags

no code implementations1 Nov 2022 Jiang Liu, Donghong Ji, Jingye Li, Dongdong Xie, Chong Teng, Liang Zhao, Fei Li

Concretely, we construct tag representations and embed them into TREM, so that TREM can treat tag and word representations as queries/keys/values and utilize self-attention to model their relationships.

named-entity-recognition Named Entity Recognition +2

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

Retinal Structure Detection in OCTA Image via Voting-based Multi-task Learning

1 code implementation23 Aug 2022 Jinkui Hao, Ting Shen, Xueli Zhu, Yonghuai Liu, Ardhendu Behera, Dan Zhang, Bang Chen, Jiang Liu, Jiong Zhang, Yitian Zhao

Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making.

Classification Decision Making +1

SuperVessel: Segmenting High-resolution Vessel from Low-resolution Retinal Image

1 code implementation28 Jul 2022 Yan Hu, Zhongxi Qiu, Dan Zeng, Li Jiang, Chen Lin, Jiang Liu

Vascular segmentation extracts blood vessels from images and serves as the basis for diagnosing various diseases, like ophthalmic diseases.

Medical Image 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

Siamese Encoder-based Spatial-Temporal Mixer for Growth Trend Prediction of Lung Nodules on CT Scans

1 code implementation7 Jun 2022 Jiansheng Fang, Jingwen Wang, Anwei Li, Yuguang Yan, Yonghe Hou, Chao Song, Hongbo Liu, Jiang Liu

In the management of lung nodules, we are desirable to predict nodule evolution in terms of its diameter variation on Computed Tomography (CT) scans and then provide a follow-up recommendation according to the predicted result of the growing trend of the nodule.

Computed Tomography (CT) Management

One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputation

no code implementations28 Apr 2022 Jiang Liu, Srivathsa Pasumarthi, Ben Duffy, Enhao Gong, Keshav Datta, Greg Zaharchuk

In this work, we formulate missing data imputation as a sequence-to-sequence learning problem and propose a multi-contrast multi-scale Transformer (MMT), which can take any subset of input contrasts and synthesize those that are missing.

Image Generation Imputation

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.

Deep Learning for Computational Cytology: A Survey

no code implementations10 Feb 2022 Hao Jiang, Yanning Zhou, Yi Lin, Ronald CK Chan, Jiang Liu, Hao Chen

Computational cytology is a critical, rapid-developing, yet challenging topic in the field of medical image computing which analyzes the digitized cytology image by computer-aided technologies for cancer screening.

Transfer Learning

SS3D: Sparsely-Supervised 3D Object Detection From Point Cloud

no code implementations CVPR 2022 Chuandong Liu, Chenqiang Gao, Fangcen Liu, Jiang Liu, Deyu Meng, Xinbo Gao

In the meantime, we design a reliable background mining module and a point cloud filling data augmentation strategy to generate the confident data for iteratively learning with reliable supervision.

3D Object Detection Data Augmentation +1

Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection

1 code implementation CVPR 2022 Jiang Liu, Alexander Levine, Chun Pong Lau, Rama Chellappa, Soheil Feizi

In addition, we design a robust shape completion algorithm, which is guaranteed to remove the entire patch from the images if the outputs of the patch segmenter are within a certain Hamming distance of the ground-truth patch masks.

Adversarial Attack Detection Adversarial Defense +4

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

Weighing Features of Lung and Heart Regions for Thoracic Disease Classification

no code implementations26 May 2021 Jiansheng Fang, Yanwu Xu, Yitian Zhao, Yuguang Yan, Junling Liu, Jiang Liu

By zeroing features of non-lung and heart regions in attention maps, we can effectively exploit their disease-specific cues in lung and heart regions.

Binarization Thoracic Disease Classification

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

3D Vessel Reconstruction in OCT-Angiography via Depth Map Estimation

no code implementations26 Feb 2021 Shuai Yu, Jianyang Xie, Jinkui Hao, Yalin Zheng, Jiong Zhang, Yan Hu, Jiang Liu, Yitian Zhao

Experimental results demonstrate that our method is effective in the depth prediction and 3D vessel reconstruction for OCTA images.% results may be used to guide subsequent vascular analysis

Decision Making Depth Estimation +3

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

Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey

no code implementations9 Dec 2020 Xiaoqing Zhang, Yan Hu, Zunjie Xiao, Jiansheng Fang, Risa Higashita, Jiang Liu

This survey provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic images.

BIG-bench Machine Learning Classification +1

Attention-based Saliency Hashing for Ophthalmic Image Retrieval

1 code implementation7 Dec 2020 Jiansheng Fang, Yanwu Xu, Xiaoqing Zhang, Yan Hu, Jiang Liu

The different grades or classes of ophthalmic images may be share similar overall performance but have subtle differences that can be differentiated by mining salient regions.

Image Retrieval Retrieval

Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference

1 code implementation7 Dec 2020 Jiansheng Fang, Xiaoqing Zhang, Yan Hu, Yanwu Xu, Ming Yang, Jiang Liu

Latent Factor Model (LFM) is one of the most successful methods for Collaborative filtering (CF) in the recommendation system, in which both users and items are projected into a joint latent factor space.

Bayesian Inference Collaborative Filtering +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.


Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images

1 code implementation ECCV 2020 Kang Zhou, Yuting Xiao, Jianlong Yang, Jun Cheng, Wen Liu, Weixin Luo, Zaiwang Gu, Jiang Liu, Shenghua Gao

In the end, we further utilize the reconstructed image to extract the structure and measure the difference between structure extracted from original and the reconstructed image.

Anatomy Anomaly Detection +1

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

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

A Two-Stream Meticulous Processing Network for Retinal Vessel Segmentation

no code implementations15 Jan 2020 Shaoming Zheng, Tianyang Zhang, Jiawei Zhuang, Hao Wang, Jiang Liu

In this paper, we propose a novel two-stream Meticulous-Processing Network (MP-Net) for tackling this problem.

Retinal Vessel Segmentation

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

GetNet: Get Target Area for Image Pairing

no code implementations8 Oct 2019 Henry H. Yu, Jiang Liu, Hao Sun, Ziwen Wang, Haotian Zhang

Image pairing is an important research task in the field of computer vision.

Person Re-Identification

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

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.

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

Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading

no code implementations31 Aug 2018 Kang Zhou, Zaiwang Gu, Wen Liu, Weixin Luo, Jun Cheng, Shenghua Gao, Jiang Liu

To considering the relationships of images with different stages, we propose a \textbf{Multi-Task} learning strategy which predicts the label with both classification and regression.

Diabetic Retinopathy Grading General Classification +1

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.

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.

A novel learning-based frame pooling method for Event Detection

no code implementations7 Mar 2016 Lan Wang, Chenqiang Gao, Jiang Liu, Deyu Meng

Detecting complex events in a large video collection crawled from video websites is a challenging task.

Event Detection

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.

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