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20212026
most citedAADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation

93 citations · 133 across the 27 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

eess.IV2024

Diff4MMLiTS: Advanced Multimodal Liver Tumor Segmentation via Diffusion-Based Image Synthesis and Alignment

Shiyun Chen, Li Lin, Pujin Cheng +5

Multimodal learning has been demonstrated to enhance performance across various clinical tasks, owing to the diverse perspectives offered by different modalities of data. However,…

cs.CV2024★ 1 cited

AniMer: Animal Pose and Shape Estimation Using Family Aware Transformer

Jin Lyu, Tianyi Zhu, Yi Gu +5

Quantitative analysis of animal behavior and biomechanics requires accurate animal pose and shape estimation across species, and is important for animal welfare and biological rese…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024★ 1 cited

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…

cs.CV2024★ 1 cited

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…