FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise. Federated learning (FL) has emerged as a promising paradigm for training segmentation models on decentralized medical data, owing to its privacy-preserving property.
Dec 20, 2023
This paper proposes Federated learning with Annotation quAlity-aware AggregatIon, named FedA3I, by introducing a quality factor based on client-wise noise ...
GitHub - wnn2000/FedAAAI: Official implementation of the paper "FedA3I
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This is the official implementation for the paper: "FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous ...
Mar 25, 2024 · FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise. Nannan Wu ...
FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise. N Wu, Z Sun, Z Yan, L Yu.
FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise. Proceedings of the AAAI ...
FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise · 1 code implementation • 20 Dec ...
Oct 21, 2024 · FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise. AAAI 2024 ...
Oct 7, 2024 · Our insight is to conceptualize incomplete annotations as noisy data (i.e., low-quality data), with a focus on mitigating their adverse effects.
Oct 6, 2024 · FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise. Article. Mar ...