We use combined margin loss function to train on the computationally efficient PeleeNet architecture to extract features that exhibit: (1) Intra-phase similarity, (2) Inter-phase dissimilarity. 
PeleeNet
Using these features, we propose to encapsulate sequential feature embeddings, 64 at a time and classify the surgical phase based on customized efficient residual factorized CNN architecture (ST-ERFNet). 
ST-ERFNet
Spatio-Temporal Features Based Surgical Phase Classification Using CNNs.In this paper, we propose a novel encoder-decoder based surgical phase classification technique leveraging on the spatio-temporal features extracted from the videos of laparoscopic cholecystectomy surgery. 
CNNs