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Surgical workflow analysis is of importance for understanding onset andpersistence of surgical phases and individual tool usage across surgery and ineach phase. It is beneficial for clinical quality control and to hospitaladministrators for understanding surgery planning. Video acquired duringsurgery typically can be leveraged for this task. Currently, a combination ofconvolutional neural network (CNN) and recurrent neural networks (RNN) arepopularly used for video analysis in general, not only being restricted tosurgical videos. In this paper, we propose a multi-task learning frameworkusing CNN followed by a bi-directional long short term memory (Bi-LSTM) tolearn to encapsulate both forward and backward temporal dependencies. Further,the joint distribution indicating set of tools associated with a phase is usedas an additional loss during learning to correct for their co-occurrence in anypredictions. Experimental evaluation is performed using the Cholec80 dataset.We report a mean average precision (mAP) score of 0.99 and 0.86 for tool andphase identification respectively which are higher compared to prior-art in thefield.

Multitask Learning of Temporal Connectionism in Convolutional Networks  using a Joint Distribution Loss Function to Simultaneously Identify Tools and  Phase in Surgical Videos