Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Motivation Large-scale neuronal morphologies are essential to neuronal ty**, connectivity
characterization and brain modeling. It is widely accepted that automation is critical to the …

This survey presents a review of state-of-the-art deep neural network architectures,
algorithms, and systems in speech and vision applications. Recent advances in deep …

Accurate liver tumor segmentation without contrast agents (non-enhanced images) avoids
the contrast-agent-associated time-consuming and high risk, which offers radiologists quick …

Mesoscale microscopy images of the brain contain a wealth of information which can help
us understand the working mechanisms of the brain. However, it is a challenging task to …

Digital reconstruction of neuronal structures from 3D microscopy images is critical for the
quantitative investigation of brain circuits and functions. It is a challenging task that would …

The study of brain connectivity has been a cornerstone in understanding the complexities of
neurological and psychiatric disorders. It has provided invaluable insights into the functional …

Extracting the cerebral anterior vessel tree of patients with an intracranial large vessel
occlusion (LVO) is relevant to investigate potential biomarkers that can contribute to …

Machine learning for biomedical imaging often suffers from a lack of labelled training data.
One solution is to use generative models to synthesise more data. To this end, we introduce …

The diagnostic value of ultrasound images may be limited by the presence of artefacts,
notably acoustic shadows, lack of contrast and localised signal dropout. Some of these …