Agricultural analysis based on Copernicus data

Exascale learning on medical image data aims at improving the performance of decision support in the process of cancer detection, localisation and staging/grading to optimize and personalize treatment planning. The automated tools  deliver decision support for physicians, speed up the visual inspection of the tissue specimens and reduce the subjectivity in the grading/staging process.

The use case tackles cancer detection and tissue classification on the latest challenges in cancer research using histopathology images, such as CAMELYON and TUPAC. The PROCESS infrastructure allows to develop more complex models and to use increasingly larger amounts of data at a finer scale.

The access to exascale computing will consistently decrease the turn-around time of the experiments, pushing the computation boundaries and consequently allowing researchers to develop models that are otherwise computationally unfeasible.