A transfer learning method for generating features suitable for surgical tools and phase recognition from the ImageNet classification features [1] is proposed here. In addition, methods are developed for generating contextual features and combining them with time series analysis for final classification using multi-class random forest. The proposed pipeline is tested over the training and testing datasets of M2CAI16 challenges: tool and phase detection. Encouraging results are obtained by leave-one-out cross validation evaluation on the training dataset.
@article{arxiv.1610.08854,
title = {Tool and Phase recognition using contextual CNN features},
author = {Manish Sahu and Anirban Mukhopadhyay and Angelika Szengel and Stefan Zachow},
journal= {arXiv preprint arXiv:1610.08854},
year = {2016}
}