93 citations · 125 across the 14 of their papers we have counts for
14 papers
WaRA: Wavelet Low-Rank Adaptation for Medical Image Classification
Moein Heidari, Yijin Huang, Yasamin Medghalchi +4
Adapting large pretrained vision models to medical image classification is often limited by memory, computation, and task-specific specializations. Parameter-efficient fine-tuning…
Text-guided Foundation Model Adaptation for Long-Tailed Medical Image Classification
Sirui Li, Li Lin, Yijin Huang +2
In medical contexts, the imbalanced data distribution in long-tailed datasets, due to scarce labels for rare diseases, greatly impairs the diagnostic accuracy of deep learning mode…
Boosting Memory Efficiency in Transfer Learning for High-Resolution Medical Image Classification
Yijin Huang, Pujin Cheng, Roger Tam +1
The success of large-scale pre-trained models has established fine-tuning as a standard method for achieving significant improvements in downstream tasks. However, fine-tuning the…
Saliency-guided and Patch-based Mixup for Long-tailed Skin Cancer Image Classification
Tianyunxi Wei, Yijin Huang, Li Lin +3
Medical image datasets often exhibit long-tailed distributions due to the inherent challenges in medical data collection and annotation. In long-tailed contexts, some common diseas…
Fine-grained Prompt Tuning: A Parameter and Memory Efficient Transfer Learning Method for High-resolution Medical Image Classification
Yijin Huang, Pujin Cheng, Roger Tam +1
Parameter-efficient transfer learning (PETL) is proposed as a cost-effective way to transfer pre-trained models to downstream tasks, avoiding the high cost of updating entire large…
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…
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…
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…
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…
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…