most citedFedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise

2 citations · 3 across the 3 of their papers we have counts for

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cs.LG2024

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…

cs.LG2024★ 2 cited

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…