93 citations · 121 across the 17 of their papers we have counts for
13 papers · 1 filter
Full end-to-end diagnostic workflow automation of 3D OCT via foundation model-driven AI for retinal diseases
Jinze Zhang, Jian Zhong, Li Lin +18
Optical coherence tomography (OCT) has revolutionized retinal disease diagnosis with its high-resolution and three-dimensional imaging nature, yet its full diagnostic automation in…
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…
FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation
Li Lin, Yixiang Liu, Jiewei Wu +4
Federated learning (FL) effectively mitigates the data silo challenge brought about by policies and privacy concerns, implicitly harnessing more data for deep model training. Howev…
ProCNS: Progressive Prototype Calibration and Noise Suppression for Weakly-Supervised Medical Image Segmentation
Y. Liu, L. Lin, K. K. Y. Wong +1
Weakly-supervised segmentation (WSS) has emerged as a solution to mitigate the conflict between annotation cost and model performance by adopting sparse annotation formats (e.g., p…
ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor Segmentation
Shiyun Chen, Li Lin, Pujin Cheng +1
Liver tumor segmentation is essential for computer-aided diagnosis, surgical planning, and prognosis evaluation. However, obtaining and maintaining a large-scale dataset with dense…
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…
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…
MProtoNet: A Case-Based Interpretable Model for Brain Tumor Classification with 3D Multi-parametric Magnetic Resonance Imaging
Yuanyuan Wei, Roger Tam, Xiaoying Tang
Recent applications of deep convolutional neural networks in medical imaging raise concerns about their interpretability. While most explainable deep learning applications use post…
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…