The rate of progress in human neurosciences is limited by the inability to easily apply a wide
range of analysis methods to the plethora of different datasets acquired in labs around the …

Data sharing is becoming more of a requirement as technologies mature and as global
research and communications diversify. As a result, researchers are looking for practical …

In clinical scenarios, there is an increasing interest in complex computational experiments,
as for example the training of Deep Learning models. Reproducibility is an essential …

Background Emerging neuroimaging datasets (collected with imaging techniques such as
electron microscopy, optical microscopy, or X-ray microtomography) describe the location …

Emerging neuroimaging datasets (collected through modalities such as Electron
Microscopy, Calcium Imaging, or X-ray Microtomography) describe the location and …

Convolutional Neural Networks (CNN) are used for automatic cancer detection in
pathological images. These data-driven experiments are difficult to reproduce, because the …

Applications for data analysis of biomedical data are complex programs and often consist of
multiple components. Re-usage of existing solutions from external code repositories or …

Applications for biomedical data processing often integrate external libraries and
frameworks for common algorithmic tasks. It typically reduces development time and …

In this paper, we present a deep-learning based diagnostic pipeline for breast cancer that
has been designed in the H2020 INCISIVE project. The design of the pipeline has taken into …

Animal population imaging is a domain still in its infancy that requires a similar technical
support as for human population imaging: technical solutions for storing and processing …