93 citations · 133 across the 27 of their papers we have counts for
7 papers · 1 filter
UNO-QA: An Unsupervised Anomaly-Aware Framework with Test-Time Clustering for OCTA Image Quality Assessment
Juntao Chen, Li Lin, Pujin Cheng +2
Medical image quality assessment (MIQA) is a vital prerequisite in various medical image analysis applications. Most existing MIQA algorithms are fully supervised that request a la…
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…
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…
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…
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…
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…