93 citations · 125 across the 14 of their papers we have counts for
5 papers · 1 filter
Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement
Puijin Cheng, Li Lin, Yijin Huang +3
The quality of a fundus image can be compromised by numerous factors, many of which are challenging to be appropriately and mathematically modeled. In this paper, we introduce a no…
AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation
Junyan Lyu, Yiqi Zhang, Yijin Huang +3
Convolutional neural networks have been widely applied to medical image segmentation and have achieved considerable performance. However, the performance may be significantly affec…
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.…
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…
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…