  Video is an essential imaging modality for diagnostics, e.g. in ultrasound
imaging, for endoscopy, or movement assessment. However, video hasn't received
a lot of attention in the medical image analysis community. In the clinical
practice, it is challenging to utilise raw diagnostic video data efficiently as
video data takes a long time to process, annotate or audit. In this paper we
introduce a novel, fully automatic video summarization method that is tailored
to the needs of medical video data. Our approach is framed as reinforcement
learning problem and produces agents focusing on the preservation of important
diagnostic information. We evaluate our method on videos from fetal ultrasound
screening, where commonly only a small amount of the recorded data is used
diagnostically. We show that our method is superior to alternative video
summarization methods and that it preserves essential information required by
clinical diagnostic standards.
