31 citations · 49 across the 14 of their papers we have counts for
14 papers
FedPCA: Noise-Robust Fair Federated Learning via Performance-Capacity Analysis
Nannan Wu, Zengqiang Yan, Nong Sang +2
Training a model that effectively handles both common and rare data-i.e., achieving performance fairness-is crucial in federated learning (FL). While existing fair FL methods have…
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matching
Nannan Wu, Zhuo Kuang, Zengqiang Yan +2
Despite the potential of federated learning in medical applications, inconsistent imaging quality across institutions-stemming from lower-quality data from a minority of clients-bi…
PASSION: Towards Effective Incomplete Multi-Modal Medical Image Segmentation with Imbalanced Missing Rates
Junjie Shi, Caozhi Shang, Zhaobin Sun +3
Incomplete multi-modal image segmentation is a fundamental task in medical imaging to refine deployment efficiency when only partial modalities are available. However, the common p…
FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completeness
Yangyang Xiang, Nannan Wu, Li Yu +3
Federated learning has emerged as a compelling paradigm for medical image segmentation, particularly in light of increasing privacy concerns. However, most of the existing research…
FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity
Zhaobin Sun, Nannan Wu, Junjie Shi +4
Cross-silo federated learning (FL) enables decentralized organizations to collaboratively train models while preserving data privacy and has made significant progress in medical im…
From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching
Nannan Wu, Zhuo Kuang, Zengqiang Yan +1
Due to escalating privacy concerns, federated learning has been recognized as a vital approach for training deep neural networks with decentralized medical data. In practice, it is…
SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Prompts
Xian Lin, Yangyang Xiang, Zhehao Wang +3
Segment anything model (SAM), a foundation model with superior versatility and generalization across diverse segmentation tasks, has attracted widespread attention in medical imagi…
FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise
Nannan Wu, Zhaobin Sun, Zengqiang Yan +1
Federated learning (FL) has emerged as a promising paradigm for training segmentation models on decentralized medical data, owing to its privacy-preserving property. However, exist…
ConvFormer: Plug-and-Play CNN-Style Transformers for Improving Medical Image Segmentation
Xian Lin, Zengqiang Yan, Xianbo Deng +2
Transformers have been extensively studied in medical image segmentation to build pairwise long-range dependence. Yet, relatively limited well-annotated medical image data makes tr…
Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting
Xian Lin, Yangyang Xiang, Li Yu +1
End-to-end medical image segmentation is of great value for computer-aided diagnosis dominated by task-specific models, usually suffering from poor generalization. With recent brea…
FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity
Nannan Wu, Li Yu, Xuefeng Jiang +2
Federated noisy label learning (FNLL) is emerging as a promising tool for privacy-preserving multi-source decentralized learning. Existing research, relying on the assumption of cl…
The Lighter The Better: Rethinking Transformers in Medical Image Segmentation Through Adaptive Pruning
Xian Lin, Li Yu, Kwang-Ting Cheng +1
Vision transformers have recently set off a new wave in the field of medical image analysis due to their remarkable performance on various computer vision tasks. However, recent hy…
BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentation
Xian Lin, Li Yu, Kwang-Ting Cheng +1
Objective: Transformers, born to remedy the inadequate receptive fields of CNNs, have drawn explosive attention recently. However, the daunting computational complexity of global r…
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification
Nannan Wu, Li Yu, Xin Yang +2
Federated learning (FL), training deep models from decentralized data without privacy leakage, has shown great potential in medical image computing recently. However, considering t…