Predicting drug side-effects before they occur is a key task in keeping the number of drug-related hospitalizations low and to improve drug discovery processes.
Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes.
Multi-organ segmentation of X-ray images is of fundamental importance for computer aided diagnosis systems.
The use of graph neural networks maximizes the information in input at each generative step, which consists of the subgraph produced during the previous steps.
Providing pixel-level supervisions for scene text segmentation is inherently difficult and costly, so that only few small datasets are available for this task.
In this paper, we use Generative Adversarial Networks (GANs) for synthesizing high quality retinal images, along with the corresponding semantic label-maps, to be used instead of real images during the training process.
The generated annotations are used to train a deep convolutional neural network for semantic segmentation.
In this paper, we study how to model taxi drivers' behaviour and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey.