1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…
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…