@InProceedings{PetscharnigMMM17, author = {Petscharnig, Stefan and Schoeffmann, Klaus}, booktitle = {International Conference on Multimedia Modeling}, title = {Deep Learning of Shot Classification in Gynecologic Surgery Videos}, year = {2017}, address = {Cham}, editor = {Amsaleg, Laurent and Guðmundsson, Gylfi Þór and Gurrin, Cathal and Jónsson, Björn Þór and Satoh, Shin’ichi}, month = {jan}, pages = {702-713}, publisher = {Springer}, abstract = {In the last decade, advances in endoscopic surgery resulted in vast amounts of video data which is used for documentation, analysis, and education purposes. In order to find video scenes relevant for aforementioned purposes, physicians manually search and annotate hours of endoscopic surgery videos. This process is tedious and time-consuming, thus motivating the (semi-)automatic annotation of such surgery videos. In this work, we want to investigate whether the single-frame model for semantic surgery shot classification is feasible and useful in practice. We approach this problem by further training of AlexNet, an already pre-trained CNN architecture. Thus, we are able to transfer knowledge gathered from the Imagenet database to the medical use case of shot classification in endoscopic surgery videos. We annotate hours of endoscopic surgery videos for training and testing data. Our results imply that the CNN-based single-frame classification approach is able to provide useful suggestions to medical experts while annotating video scenes. Hence, the annotation process is consequently improved. Future work shall consider the evaluation of more sophisticated classification methods incorporating the temporal video dimension, which is expected to improve on the baseline evaluation done in this work.}, edition = {LNCS 10132}, keywords = {Multimedia content analysis, Convolutional neural networks, Deep learning, Medical shot classification}, language = {EN}, location = {Klagenfurt, Austria}, talkdate = {2017.01.05}, talktype = {registered}, url = {https://link.springer.com/chapter/10.1007/978-3-319-51811-4_57} }