93 citations · 133 across the 27 of their papers we have counts for
17 papers · 1 filter
CRISP: Constrained Refinement via Iterative Squeezing Process for Robust Medical Image Segmentation under Domain Shift
Yizhou Fang, Pujin Cheng, Yixiang Liu +2
Distribution shift in medical imaging remains a central bottleneck for the clinical translation of medical AI. Failure to address it can lead to severe performance degradation in u…
CRISP: Rank-Guided Iterative Squeezing for Robust Medical Image Segmentation under Domain Shift
Yizhou Fang, Pujin Cheng, Yixiang Liu +2
Distribution shift in medical imaging remains a central bottleneck for the clinical translation of medical AI. Failure to address it can lead to severe performance degradation in u…
4DEquine: Disentangling Motion and Appearance for 4D Equine Reconstruction from Monocular Video
Jin Lyu, Liang An, Pujin Cheng +2
4D reconstruction of equine family (e.g. horses) from monocular video is important for animal welfare. Previous mainstream 4D animal reconstruction methods require joint optimizati…
UniFS: Unified Multi-Contrast MRI Reconstruction via Frequency-Spatial Fusion
Jialin Li, Yiwei Ren, Kai Pan +4
Recently, Multi-Contrast MR Reconstruction (MCMR) has emerged as a hot research topic that leverages high-quality auxiliary modalities to reconstruct undersampled target modalities…
AniMer+: Unified Pose and Shape Estimation Across Mammalia and Aves via Family-Aware Transformer
Liang An, Jin Lyu, Li Lin +3
In the era of foundation models, achieving a unified understanding of different dynamic objects through a single network has the potential to empower stronger spatial intelligence.…
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…
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…
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…
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…
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…
LDDMM-Face: Large Deformation Diffeomorphic Metric Learning for Flexible and Consistent Face Alignment
Huilin Yang, Junyan Lyu, Pujin Cheng +1
We innovatively propose a flexible and consistent face alignment framework, LDDMM-Face, the key contribution of which is a deformation layer that naturally embeds facial geometry i…
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…