Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …

The volume of medical images stored in hospitals is rapidly increasing; however, the
utilization of these accumulated medical images remains limited. Existing content-based …

Recently, it has been shown that CycleGANs are masters of steganography. They cannot
only learn reliable mappings between two distributions without calling for paired training …

Pseudo-healthy synthesis is the task of creating a subject-specific 'healthy'image from a
pathological one. Such images can be helpful in tasks such as anomaly detection and …

Over the past years, pseudo-healthy reconstruction for unsupervised anomaly detection has
gained in popularity. This approach has the great advantage of not requiring tedious pixel …

Brain tissue segmentation from multimodal MRI is a key building block of many
neuroimaging analysis pipelines. Established tissue segmentation approaches have …

Brain ageing is associated with morphological changes and cognitive degeneration, and
can be affected by neurodegenerative diseases which can accelerate the ageing process …

Contrast materials are often injected into body to contrast specific tissues in Computed
Tomography (CT) images. Contrast Enhanced CT (CECT) images obtained in this way are …

Unsupervised anomaly detection using deep learning models is a popular computer-aided
diagnosis approach because it does not need annotated data and is not restricted to the …

In this paper we propose a model for controllable synthesis of 3D (volumetric) medical
image data. The model is comprised of three components which are learnt simultaneously …