%0 Journal Article %T Knowledge transfer for surgical activity prediction %+ Laboratoire Traitement du Signal et de l'Image (LTSI) %+ CHU Pontchaillou [Rennes] %A Dergachyova, Olga %A Morandi, Xavier %A Jannin, Pierre %Z ANR-11-LABX-0004, Agence Nationale de la Recherche %< avec comité de lecture %@ 1861-6410 %J International Journal of Computer Assisted Radiology and Surgery %I Springer Verlag %V 13 %N 9 %P 1409-1417 %8 2018-04-23 %D 2018 %R 10.1007/s11548-018-1768-9 %M 29687177 %K Knowledge transfer %K Long Short-Term Memory %K Surgical activity prediction %K Transfer learning %K Word embedding %Z Life Sciences [q-bio]/BioengineeringJournal articles %X Purpose - Lack of annotated training data hinders automatic recognition and prediction of surgical activities necessary for situation-aware operating rooms. We propose using knowledge transfer to compensate for data deficit and improve prediction. Methods - We used two approaches to extract and transfer surgical process knowledge. First, we encoded semantic information about surgical terms using word embedding. Secondly, we passed knowledge between different clinical datasets of neurosurgical procedures using transfer learning. Results - The combination of two methods provided 22% improvement of activity prediction. We also made several pertinent observations about surgical practices based on the results of the performed transfer. Conclusion - Word embedding boosts learning process. Transfer learning was shown to be more effective than a simple combination of data, especially for less similar procedures. %G English %L hal-01807370 %U https://univ-rennes.hal.science/hal-01807370 %~ UNIV-RENNES1 %~ LTSI %~ HL %~ STATS-UR1 %~ LTSI-MEDICIS %~ UR1-HAL %~ UR1-MATH-STIC %~ TEST-UR-CSS %~ UNIV-RENNES %~ ANR %~ UR1-MATH-NUM %~ UR1-BIO-SA