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.

基于弱监督的增强学习系统用于局部化。通过引入一种新的奖励定义，我们训练了一个控制器函数，以定位图像中感兴趣的区域，并利用预训练的二元分类器生成的非二进制分类概率。我们使用肿瘤病变局部化任务在临床前列腺多参数磁共振图像的大型数据集上评估了我们提出的方法。与常用的多实例学习弱监督局部化和完全监督基线相比，我们的方法优于多实例学习，并且仅使用图像级别的分类标签进行训练，与完全监督学习表现相当。