  In this paper we propose a reinforcement learning based weakly supervised
system for localisation. We train a controller function to localise regions of
interest within an image by introducing a novel reward definition that utilises
non-binarised classification probability, generated by a pre-trained binary
classifier which classifies object presence in images or image crops. The
object-presence classifier may then inform the controller of its localisation
quality by quantifying the likelihood of the image containing an object. Such
an approach allows us to minimize any potential labelling or human bias
propagated via human labelling for fully supervised localisation. We evaluate
our proposed approach for a task of cancerous lesion localisation on a large
dataset of real clinical bi-parametric MR images of the prostate. Comparisons
to the commonly used multiple-instance learning weakly supervised localisation
and to a fully supervised baseline show that our proposed method outperforms
the multi-instance learning and performs comparably to fully-supervised
learning, using only image-level classification labels for training.
