93 citations · 133 across the 27 of their papers we have counts for
27 papers
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.…
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,…
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…
Super-Resolution on Rotationally Scanned Photoacoustic Microscopy Images Incorporating Scanning Prior
Kai Pan, Linyang Li, Li Lin +4
Photoacoustic Microscopy (PAM) images integrating the advantages of optical contrast and acoustic resolution have been widely used in brain studies. However, there exists a trade-o…
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…
JOINEDTrans: Prior Guided Multi-task Transformer for Joint Optic Disc/Cup Segmentation and Fovea Detection
Huaqing He, Li Lin, Zhiyuan Cai +2
Deep learning-based image segmentation and detection models have largely improved the efficiency of analyzing retinal landmarks such as optic disc (OD), optic cup (OC), and fovea.…
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…
DS3-Net: Difficulty-perceived Common-to-T1ce Semi-Supervised Multimodal MRI Synthesis Network
Ziqi Huang, Li Lin, Pujin Cheng +2
Contrast-enhanced T1 (T1ce) is one of the most essential magnetic resonance imaging (MRI) modalities for diagnosing and analyzing brain tumors, especially gliomas. In clinical prac…
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion
Ziqi Huang, Li Lin, Pujin Cheng +2
Multi-modal magnetic resonance (MR) imaging provides great potential for diagnosing and analyzing brain gliomas. In clinical scenarios, common MR sequences such as T1, T2 and FLAIR…
Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation
Linkai Peng, Li Lin, Pujin Cheng +2
Unsupervised domain adaptation has been proposed recently to tackle the so-called domain shift between training data and test data with different distributions. However, most of th…
Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning
Linkai Peng, Li Lin, Pujin Cheng +2
Various deep learning models have been developed to segment anatomical structures from medical images, but they typically have poor performance when tested on another target domain…
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…