Knowledge transfer for surgical activity prediction
This work addresses data scarcity in surgical activity recognition for situation-aware operating rooms, but it is incremental as it applies existing transfer learning techniques to a specific domain.
The paper tackled the problem of limited training data for surgical activity prediction by using knowledge transfer methods, resulting in a 22% improvement in prediction accuracy.
Lack of 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. We used two approaches to extract and transfer surgical process knowledge. First, we encoded semantic information about surgical terms using word embedding which boosted learning process. Secondly, we passed knowledge between different clinical datasets of neurosurgical procedures using transfer learning. Transfer learning was shown to be more effective than a simple combination of data, especially for less similar procedures. The combination of two methods provided 22% improvement of activity prediction. We also made several pertinent observations about surgical practices.