51 citations · 128 across the 22 of their papers we have counts for
22 papers
DS3-Net: Difficulty-perceived Common-to-T1ce Semi-Supervised Multimodal MRI Synthesis Network
Ziqi Huang, Li Lin, Pujin Cheng +2
Contrast-enhanced T1 (T1ce) is one of the most essential magnetic resonance imaging (MRI) modalities for diagnosing and analyzing brain tumors, especially gliomas. In clinical prac…
LesionPaste: One-Shot Anomaly Detection for Medical Images
Weikai Huang, Yijin Huang, Xiaoying Tang
Due to the high cost of manually annotating medical images, especially for large-scale datasets, anomaly detection has been explored through training models with only normal data.…
Uni4Eye: Unified 2D and 3D Self-supervised Pre-training via Masked Image Modeling Transformer for Ophthalmic Image Classification
Zhiyuan Cai, Li Lin, Huaqing He +1
A large-scale labeled dataset is a key factor for the success of supervised deep learning in computer vision. However, a limited number of annotated data is very common, especially…
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion
Ziqi Huang, Li Lin, Pujin Cheng +2
Multi-modal magnetic resonance (MR) imaging provides great potential for diagnosing and analyzing brain gliomas. In clinical scenarios, common MR sequences such as T1, T2 and FLAIR…
Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation
Linkai Peng, Li Lin, Pujin Cheng +2
Unsupervised domain adaptation has been proposed recently to tackle the so-called domain shift between training data and test data with different distributions. However, most of th…
JOINED : Prior Guided Multi-task Learning for Joint Optic Disc/Cup Segmentation and Fovea Detection
Huaqing He, Li Lin, Zhiyuan Cai +1
Fundus photography has been routinely used to document the presence and severity of various retinal degenerative diseases such as age-related macula degeneration, glaucoma, and dia…
GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges
Junde Wu, Huihui Fang, Fei Li +26
Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both two modalities of images have prominent biomarker…
COROLLA: An Efficient Multi-Modality Fusion Framework with Supervised Contrastive Learning for Glaucoma Grading
Zhiyuan Cai, Li Lin, Huaqing He +1
Glaucoma is one of the ophthalmic diseases that may cause blindness, for which early detection and treatment are very important. Fundus images and optical coherence tomography (OCT…
Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning
Linkai Peng, Li Lin, Pujin Cheng +2
Various deep learning models have been developed to segment anatomical structures from medical images, but they typically have poor performance when tested on another target domain…
Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation
Yijin Huang, Li Lin, Pujin Cheng +3
Although deep learning based diabetic retinopathy (DR) classification methods typically benefit from well-designed architectures of convolutional neural networks, the training sett…
Automatic hippocampal surface generation via 3D U-net and active shape modeling with hybrid particle swarm optimization
Pinyuan Zhong, Yue Zhang, Xiaoying Tang
In this paper, we proposed and validated a fully automatic pipeline for hippocampal surface generation via 3D U-net coupled with active shape modeling (ASM). Principally, the propo…
LDDMM-Face: Large Deformation Diffeomorphic Metric Learning for Flexible and Consistent Face Alignment
Huilin Yang, Junyan Lyu, Pujin Cheng +1
We innovatively propose a flexible and consistent face alignment framework, LDDMM-Face, the key contribution of which is a deformation layer that naturally embeds facial geometry i…
Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images
Yijin Huang, Li Lin, Pujin Cheng +2
Manually annotating medical images is extremely expensive, especially for large-scale datasets. Self-supervised contrastive learning has been explored to learn feature representati…
BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images
Li Lin, Zhonghua Wang, Jiewei Wu +5
Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging technique that allows visualizations of vasculature and foveal avascular zone (FAZ) across retinal l…
DRR4Covid: Learning Automated COVID-19 Infection Segmentation from Digitally Reconstructed Radiographs
Pengyi Zhang, Yunxin Zhong, Yulin Deng +2
Automated infection measurement and COVID-19 diagnosis based on Chest X-ray (CXR) imaging is important for faster examination. We propose a novel approach, called DRR4Covid, to lea…
Learning Diagnosis of COVID-19 from a Single Radiological Image
Pengyi Zhang, Yunxin Zhong, Xiaoying Tang +2
Radiological image is currently adopted as the visual evidence for COVID-19 diagnosis in clinical. Using deep models to realize automated infection measurement and COVID-19 diagnos…
Coarse-to-fine Kidney Segmentation Framework Incorporating with Abnormal Detection and Correction
Yue Zhang, Jiong Wu, Yu Zhou +2
In this paper, we formulated the kidney segmentation task in a coarse-to-fine fashion, predicting a coarse label based on the entire CT image and a fine label based on the coarse s…
A Survey on Deep Learning of Small Sample in Biomedical Image Analysis
Pengyi Zhang, Yunxin Zhong, Yulin Deng +2
The success of deep learning has been witnessed as a promising technique for computer-aided biomedical image analysis, due to end-to-end learning framework and availability of larg…
Prostate segmentation using Z-net
Yue Zhang, Jiong Wu, Wanli Chen +2
In this paper, we proposed a novel architecture of convolutional neural network (CNN), namely Z-net, for segmenting prostate from magnetic resonance images (MRIs). In the proposed…
Brain segmentation based on multi-atlas guided 3D fully convolutional network ensembles
Jiong Wu, Xiaoying Tang
In this study, we proposed and validated a multi-atlas guided 3D fully convolutional network (FCN) ensemble model (M-FCN) for segmenting brain regions of interest (ROIs) from struc…
A post-processing method to improve the white matter hyperintensity segmentation accuracy for randomly-initialized U-net
Yue Zhang, Wanli Chen, Yifan Chen +1
White matter hyperintensity (WMH) is commonly found in elder individuals and appears to be associated with brain diseases. U-net is a convolutional network that has been widely use…
Prostate Segmentation using 2D Bridged U-net
Wanli Chen, Yue Zhang, Junjun He +4
In this paper, we focus on three problems in deep learning based medical image segmentation. Firstly, U-net, as a popular model for medical image segmentation, is difficult to trai…