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Found 4 papers, 3 papers with code
Semi-supervised Pathology Segmentation with Disentangled Representations
The model is trained in a semi-supervised fashion with new reconstruction losses directly aiming to improve pathology segmentation with limited annotations.
Disentangle, align and fuse for multimodal and semi-supervised image segmentation
Core to our method is learning a disentangled decomposition into anatomical and imaging factors.
A Distance Oriented Kalman Filter Particle Swarm Optimizer Applied to Multi-Modality Image Registration
In this paper we describe improvements to the particle swarm optimizer (PSO) made by inclusion of an unscented Kalman filter to guide particle motion.
Factorised spatial representation learning: application in semi-supervised myocardial segmentation
Specifically, we achieve comparable performance to fully supervised networks using a fraction of labelled images in experiments on ACDC and a dataset from Edinburgh Imaging Facility QMRI.
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