181 research outputs found
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning
Currently, graph learning models are indispensable tools to help researchers
explore graph-structured data. In academia, using sufficient training data to
optimize a graph model on a single device is a typical approach for training a
capable graph learning model. Due to privacy concerns, however, it is
infeasible to do so in real-world scenarios. Federated learning provides a
practical means of addressing this limitation by introducing various
privacy-preserving mechanisms, such as differential privacy (DP) on the graph
edges. However, although DP in federated graph learning can ensure the security
of sensitive information represented in graphs, it usually causes the
performance of graph learning models to degrade. In this paper, we investigate
how DP can be implemented on graph edges and observe a performance decrease in
our experiments. In addition, we note that DP on graph edges introduces noise
that perturbs graph proximity, which is one of the graph augmentations in graph
contrastive learning. Inspired by this, we propose leveraging graph contrastive
learning to alleviate the performance drop resulting from DP. Extensive
experiments conducted with four representative graph models on five widely used
benchmark datasets show that contrastive learning indeed alleviates the models'
DP-induced performance drops.Comment: Accepted by Information Science
Improving Catheter Segmentation & Localization in 3D Cardiac Ultrasound Using Direction-Fused FCN
Fast and accurate catheter detection in cardiac catheterization using
harmless 3D ultrasound (US) can improve the efficiency and outcome of the
intervention. However, the low image quality of US requires extra training for
sonographers to localize the catheter. In this paper, we propose a catheter
detection method based on a pre-trained VGG network, which exploits 3D
information through re-organized cross-sections to segment the catheter by a
shared fully convolutional network (FCN), which is called a Direction-Fused FCN
(DF-FCN). Based on the segmented image of DF-FCN, the catheter can be localized
by model fitting. Our experiments show that the proposed method can
successfully detect an ablation catheter in a challenging ex-vivo 3D US
dataset, which was collected on the porcine heart. Extensive analysis shows
that the proposed method achieves a Dice score of 57.7%, which offers at least
an 11.8 % improvement when compared to state-of-the-art instrument detection
methods. Due to the improved segmentation performance by the DF-FCN, the
catheter can be localized with an error of only 1.4 mm.Comment: ISBI 2019 accepte
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