Image reconstruction for positron emission tomography (PET) has been developed over many decades, starting out with filtered backprojection methods, with advances coming from improved modelling of the data statistics and improved modelling of the overall physics of the data acquisition / imaging process. However, high noise and limited spatial resolution have remained major issues in PET, and...
Convolutional Neural Networks (CNNs) have been effectively applied in many studies where crucial information about the data is embedded in the order of features (e.g. images). However, most tabular data – such as raw Positron Emission Tomography (PET) data – do not assume a spatial correlation between features, and hence are unsuitable for CNNs classification. In order to use the power of CNNs...
Big variety of medical data types and their complex structure may be a challenge for data scientists. The process of creating the data is usually time-consuming, while access to medical facilities databases is limited due to privacy issues.
Generative models can be of great help in the process of data augmentation. The presentation will contain the idea, current status and results of...
