This paper explores the use of self-supervised deep learning in medical imaging in cases where two scan modalities are available for the same subject.
An alternative to anonymization is sharing a synthetic dataset that bears a behaviour similar to the real data but preserves privacy.
We propose a novel convolutional method for the detection and identification of vertebrae in whole spine MRIs.
In this paper we introduce the Ladder Algorithm; a novel recurrent algorithm to detect repetitive structures in natural images with high accuracy using little training data.
We show that the performance of the pre-trained CNN on the supervised classification task is (i) superior to that of a network trained from scratch; and (ii) requires far fewer annotated training samples to reach an equivalent performance to that of the network trained from scratch.
To achieve this we propose an encoder-decoder CNN model that uses a joint embedding of the face and audio to generate synthesised talking face video frames.