@InProceedings{Leibetseder2018, title = {Automatic Smoke Classification in Endoscopic Video}, author = {Andreas Leibetseder and Manfred Jürgen Primus and Klaus Schöffmann}, booktitle = {MultiMedia Modeling - 24th International Conference, MMM 2018 (Part 2)}, year = {2018}, address = {Berlin}, editor = {Klaus Schöffmann and Thanarat H. Chalidabhongse and Chong-Wah Ngo and Supavadee Aramvith and Noel E. O´Connor and Yo-Sung Ho and Moncef Gabbouj and Ahmed Elgammal}, month = {Januar}, pages = {362--366}, publisher = {Springer}, series = {LNCS}, volume = {10705}, abstract = {Medical smoke evacuation systems enable proper, filtered removal of toxic fumes during surgery, while stabilizing internal pressure during endoscopic interventions. Typically activated manually, they, however, are prone to inefficient utilization: tardy activation enables smoke to interfere with ongoing surgeries and late deactivation wastes precious resources. In order to address such issues, in this work we demonstrate a vision-based tool indicating endoscopic smoke – a first step towards automatic activation of said systems and avoiding human misconduct. In the back-end we employ a pre-trained convolutional neural network (CNN) model for distinguishing images containing smoke from others.}, doi = {10.1007/978-3-319-73600-6_33}, url = {https://link.springer.com/chapter/10.1007/978-3-319-73600-6_33} }