  Conventional photoacoustic imaging may suffer from the limited view and
bandwidth of ultrasound transducers. A deep learning approach is proposed to
handle these problems and is demonstrated both in simulations and in
experiments on a multi-scale model of leaf skeleton. We employed an
experimental approach to build the training and the test sets using photographs
of the samples as ground truth images. Reconstructions produced by the neural
network show a greatly improved image quality as compared to conventional
approaches. In addition, this work aimed at quantifying the reliability of the
neural network predictions. To achieve this, the dropout Monte-Carlo procedure
is applied to estimate a pixel-wise degree of confidence on each predicted
picture. Last, we address the possibility to use transfer learning with
simulated data in order to drastically limit the size of the experimental
dataset.
