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<https://discovery-pp.ucl.ac.uk/id/eprint/10111216> <http://purl.org/dc/terms/title> "Multi-Task Recurrent Neural Network for Surgical Gesture Recognition and Progress Prediction"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery-pp.ucl.ac.uk/id/eprint/10111216> <http://purl.org/ontology/bibo/abstract> "Surgical gesture recognition is important for surgical data science and computer-aided intervention. Even with\r\nrobotic kinematic information, automatically segmenting surgical steps presents numerous challenges because surgical demonstrations are characterized by high variability in style, duration\r\nand order of actions. In order to extract discriminative features\r\nfrom the kinematic signals and boost recognition accuracy, we\r\npropose a multi-task recurrent neural network for simultaneous\r\nrecognition of surgical gestures and estimation of a novel\r\nformulation of surgical task progress. To show the effectiveness\r\nof the presented approach, we evaluate its application on\r\nthe JIGSAWS dataset, that is currently the only publicly\r\navailable dataset for surgical gesture recognition featuring robot\r\nkinematic data. We demonstrate that recognition performance\r\nimproves in multi-task frameworks with progress estimation\r\nwithout any additional manual labelling and training"^^<http://www.w3.org/2001/XMLSchema#string> .
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