Learning recognition of semantically relevant video segments from endoscopy videos contributed and edited in a private social network
Desara Xhura · 2014
Besides the great benefit of minimizing intrusions made in body, endoscopic surgery has the advantage of producing abundant documentation regarding the procedure as well. Recordings can be used not only to document the surgery but as a mean for learning and improving experts knowledge too. To minimize time and effort that experts invest in preparing informative endoscopic videos, tools that can automatically identify interesting parts in videos are needed. To achieve this, an annotated data set is required. This paper presents an approach for collecting endoscopic videos and related experts knowledge. For this, a social network with integrated video annotation and presentation tools is used. Experts can upload, annotate and share their videos with other physicians. In the background their interactions with the videos are recorded, interpreted and used to derive predictive models or improve existing ones. Once a prediction model is derived, its results will be displayed to physicians as suggestions, which can be integrated into their video annotations. Physicians choice to either keep these suggestions or discard them will serve as a feedback to the learned model and used to refine the derived knowledge.