activity
20212023
most citedAADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation

93 citations · 117 across the 9 of their papers we have counts for

collaborators

9 papers

eess.IV2023

Super-Resolution on Rotationally Scanned Photoacoustic Microscopy Images Incorporating Scanning Prior

Kai Pan, Linyang Li, Li Lin +4

Photoacoustic Microscopy (PAM) images integrating the advantages of optical contrast and acoustic resolution have been widely used in brain studies. However, there exists a trade-o…

cs.CV2023

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

Linfeng Liu, Junyan Lyu, Siyu Liu +3

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. To accurately pr…

cs.CV2023★ 6 cited

PRIOR: Prototype Representation Joint Learning from Medical Images and Reports

Pujin Cheng, Li Lin, Junyan Lyu +3

Contrastive learning based vision-language joint pre-training has emerged as a successful representation learning strategy. In this paper, we present a prototype representation lea…

cs.CV2022★ 8 cited

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

Yijin Huang, Junyan Lyu, Pujin Cheng +2

Self-supervised Learning (SSL) has been widely applied to learn image representations through exploiting unlabeled images. However, it has not been fully explored in the medical im…

eess.IV2022★ 93 cited

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…

eess.IV2021

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…