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Found 3 papers, 1 papers with code
Architecture Analysis and Benchmarking of 3D U-shaped Deep Learning Models for Thoracic Anatomical Segmentation
We conduct the first systematic benchmark study for variants of 3D U-shaped models (3DUNet, STUNet, AttentionUNet, SwinUNETR, FocalSegNet, and a novel 3D SwinUnet with four variants) with a focus on CT-based anatomical segmentation for thoracic surgery.
VesselShot: Few-shot learning for cerebral blood vessel segmentation
Angiography is widely used to detect, diagnose, and treat cerebrovascular diseases.
Precision-Weighted Federated Learning
Our performance evaluations show 9% better predictions with MNIST, 18% with Fashion-MNIST, and 5% with CIFAR-10 in the non-IID setting.
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