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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Unsupervised Learning for Surgical Motion by Learning to Predict the Future
verfasst von : Robert DiPietro, Gregory D. Hager
Erschienen in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
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Abstract
We show that it is possible to learn meaningful representations of surgical motion, without supervision, by learning to predict the future. An architecture that combines an RNN encoder-decoder and mixture density networks (MDNs) is developed to model the conditional distribution over future motion given past motion. We show that the learned encodings naturally cluster according to high-level activities, and we demonstrate the usefulness of these learned encodings in the context of information retrieval, where a database of surgical motion is searched for suturing activity using a motion-based query. Future prediction with MDNs is found to significantly outperform simpler baselines as well as the best previously-published result for this task, advancing state-of-the-art performance from an F1 score of \(0.60 \pm 0.14\) to \(0.77 \pm 0.05\).