This repository contains easy-to-use apps for labelling neuroradiology imaging datasets on the basis of radiology reports, as detailed in our papers:
Deep learning to automate the labelling of head MRI datasets for computer vision applications, European Radiology, 2021.
Labelling imaging datasets on the basis of neuroradiology reports: a validation study, MICCAI LABELS Workshop, 2020
FastLabellerV5 is an app suitable for generating binary labels (i.e., 'normal' vs. 'abnormal'); SLowLabellerV5 is an app suitable for generating more specialised labels (i.e., 'mass'/'no mass', 'stroke'/'no stroke', 'small vessel disease'/'no small vessel disease' etc.)
These apps are tested on Windows operating system.
If you find these apps useful, please cite our work:
@incollection{wood2020labelling, title={Labelling imaging datasets on the basis of neuroradiology reports: a validation study}, author={Wood, David A and Kafiabadi, Sina and Al Busaidi, Aisha and Guilhem, Emily and Lynch, Jeremy and Townend, Matthew and Montvila, Antanas and Siddiqui, Juveria and Gadapa, Naveen and Benger, Matthew and others}, booktitle={Interpretable and Annotation-Efficient Learning for Medical Image Computing}, pages={254--265}, year={2020}, publisher={Springer} }