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Found 5 papers, 2 papers with code
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications
The real-world implementation of federated learning is complex and requires research and development actions at the crossroad between different domains ranging from data science, to software programming, networking, and security.
Are labels informative in semi-supervised learning? -- Estimating and leveraging the missing-data mechanism
Semi-supervised learning is a powerful technique for leveraging unlabeled data to improve machine learning models, but it can be affected by the presence of ``informative'' labels, which occur when some classes are more likely to be labeled than others.
Privacy Preserving Image Registration
Image registration is a key task in medical imaging applications, allowing to represent medical images in a common spatial reference frame.
Ranked #1 on
Cubic splines Image Registration
on ADNI
Affine Image Registration
Cubic splines Image Registration
+2
Don't fear the unlabelled: safe semi-supervised learning via simple debiasing
Our debiasing approach is straightforward to implement and applicable to most deep SSL methods.
GridNet with automatic shape prior registration for automatic MRI cardiac segmentation
In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge.
