7 citations · 11 across the 8 of their papers we have counts for
11 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…