Unsupervised Learning for Surgical Motion by Learning to Predict the Future

Autor:	DiPietro, Robert, Hager, Gregory D.
Rok vydání:	2018
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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 +- 0.14 to 0.77 +- 0.05. Comment: Accepted to MICCAI 2018
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1806.03318 Zobrazit plný text záznamu View this record from Arxiv