  The classification of MRI images according to the anatomical field of view is
a necessary task to solve when faced with the increasing quantity of medical
images. In parallel, advances in deep learning makes it a suitable tool for
computer vision problems. Using a common architecture (such as AlexNet)
provides quite good results, but not sufficient for clinical use. Improving the
model is not an easy task, due to the large number of hyper-parameters
governing both the architecture and the training of the network, and to the
limited understanding of their relevance. Since an exhaustive search is not
tractable, we propose to optimize the network first by random search, and then
by an adaptive search based on Gaussian Processes and Probability of
Improvement. Applying this method on a large and varied MRI dataset, we show a
substantial improvement between the baseline network and the final one (up to
20\% for the most difficult classes).
