2,034 research outputs found
Factorised spatial representation learning: application in semi-supervised myocardial segmentation
The success and generalisation of deep learning algorithms heavily depend on
learning good feature representations. In medical imaging this entails
representing anatomical information, as well as properties related to the
specific imaging setting. Anatomical information is required to perform further
analysis, whereas imaging information is key to disentangle scanner variability
and potential artefacts. The ability to factorise these would allow for
training algorithms only on the relevant information according to the task. To
date, such factorisation has not been attempted. In this paper, we propose a
methodology of latent space factorisation relying on the cycle-consistency
principle. As an example application, we consider cardiac MR segmentation,
where we separate information related to the myocardium from other features
related to imaging and surrounding substructures. We demonstrate the proposed
method's utility in a semi-supervised setting: we use very few labelled images
together with many unlabelled images to train a myocardium segmentation neural
network. 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. Code will be made available at
https://github.com/agis85/spatial_factorisation.Comment: Accepted in MICCAI 201
Informative sample generation using class aware generative adversarial networks for classification of chest Xrays
Training robust deep learning (DL) systems for disease detection from medical
images is challenging due to limited images covering different disease types
and severity. The problem is especially acute, where there is a severe class
imbalance. We propose an active learning (AL) framework to select most
informative samples for training our model using a Bayesian neural network.
Informative samples are then used within a novel class aware generative
adversarial network (CAGAN) to generate realistic chest xray images for data
augmentation by transferring characteristics from one class label to another.
Experiments show our proposed AL framework is able to achieve state-of-the-art
performance by using about of the full dataset, thus saving significant
time and effort over conventional methods
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