3.4k citations · 6.5k across the 28 of their papers we have counts for
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Transfer learning for time series classification
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber +2
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) t…
Strategies for Training Stain Invariant CNNs
Thomas Lampert, Odyssée Merveille, Jessica Schmitz +3
An important part of Digital Pathology is the analysis of multiple digitised whole slide images from differently stained tissue sections. It is common practice to mount consecutive…
Deep learning for time series classification: a review
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber +2
Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been pr…
Data augmentation using synthetic data for time series classification with deep residual networks
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber +2
Data augmentation in deep neural networks is the process of generating artificial data in order to reduce the variance of the classifier with the goal to reduce the number of error…
Surgical Data Science: A Consensus Perspective
Lena Maier-Hein, Matthias Eisenmann, Carolin Feldmann +25
Surgical data science is a scientific discipline with the objective of improving the quality of interventional healthcare and its value through capturing, organization, analysis, a…
Evaluating surgical skills from kinematic data using convolutional neural networks
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber +2
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…