We present a novel methodology combining web-based software development practices, machine learning, and spatial databases for computer-aided quantiÞcation of regions of interest (ROIs) in large-scale imaging data. We describe our main methodological choices, and then illustrate the beneÞts of the approach (workload reduction, improved precision, scalability, and traceability) on hundreds of whole-slide images of biological tissue slices in cancer research.
['Raphaël Marée', 'Loïc Rollus', 'Benjamin Stevens', 'Gilles Louppe', 'Olivier Caubo', 'Natacha Rocks', 'Sandrine Bekaert', 'Didier Cataldo', 'Louis Wehenkel']
A hybrid human-computer approach for large-scale image-based measurements using web services and machine learning