6 code implementations • 11 Sep 2019 • Hassan Ismail Fawaz, Benjamin Lucas, Germain Forestier, Charlotte Pelletier, Daniel F. Schmidt, Jonathan Weber, Geoffrey I. Webb, Lhassane Idoumghar, Pierre-Alain Muller, François Petitjean
TSC is the area of machine learning tasked with the categorization (or labelling) of time series.
The method is validated on the JIGSAWS dataset for two surgical skills evaluation tasks: classification and regression.
Over the past one hundred years, the classic teaching methodology of "see one, do one, teach one" has governed the surgical education systems worldwide.
Time Series Classification (TSC) problems are encountered in many real life data mining tasks ranging from medicine and security to human activity recognition and food safety.
Deep neural networks have revolutionized many fields such as computer vision and natural language processing.
Transfer learning for deep neural networks is the process of first training a base network on a source dataset, and then transferring the learned features (the network's weights) to a second network to be trained on a target dataset.
We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC.
This is surprising as the accuracy of deep learning models for TSC could potentially be improved, especially for small datasets that exhibit overfitting, when a data augmentation method is adopted.
The need for automatic surgical skills assessment is increasing, especially because manual feedback from senior surgeons observing junior surgeons is prone to subjectivity and time consuming.
Ranked #1 on Surgical Skills Evaluation on JIGSAWS