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Document type:
Zeitschriftenaufsatz 
Author(s):
Czempiel, T.; Paschali, M.; Keicher, M.; Simson, W.; Feußner, H.; Kim, S.T.; Navab, N. 
Title:
TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks 
Abstract:
Automatic surgical phase recognition is a challenging and crucial task with the potential to improve patient safety and become an integral part of intra-operative decision-support systems. In this paper, we propose, for the first time in workflow analysis, a Multi-Stage Temporal Convolutional Network (MS-TCN) that performs hierarchical prediction refinement for surgical phase recognition. Causal, dilated convolutions allow for a large receptive field and online inference with smooth predictions...    »
 
Keywords:
Deep Learning,Surgical Workflow,Temporal Convolutional Networks,Phase Recognition,Laparoscopic Videos,MICCAI 
Journal title:
CoRR 
Year:
2020 
Journal volume:
abs/2003.10751