Sudre, CH;
Li, W;
Vercauteren, T;
Ourselin, S;
Cardoso, MJ;
(2017)
Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations.
In:
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support.
(pp. pp. 240-248).
Springer Nature: Cham, Switzerland.
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Abstract
Deep-learning has proved in recent years to be a powerful tool for image analysis and is now widely used to segment both 2D and 3D medical images. Deep-learning segmentation frameworks rely not only on the choice of network architecture but also on the choice of loss function. When the segmentation process targets rare observations, a severe class imbalance is likely to occur between candidate labels, thus resulting in sub-optimal performance. In order to mitigate this issue, strategies such as the weighted cross-entropy function, the sensitivity function or the Dice loss function, have been proposed. In this work, we investigate the behavior of these loss functions and their sensitivity to learning rate tuning in the presence of different rates of label imbalance across 2D and 3D segmentation tasks. We also propose to use the class re-balancing properties of the Generalized Dice overlap, a known metric for segmentation assessment, as a robust and accurate deep-learning loss function for unbalanced tasks.
| Type: | Proceedings paper |
|---|---|
| Title: | Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations |
| Event: | International Workshop on Multimodal Learning for Clinical Decision Support |
| Location: | Québec City (QC), Canada |
| Dates: | 14th September 2019 |
| ISBN-13: | 978-3-319-67557-2 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-319-67558-9_28 |
| Publisher version: | http://dx.doi.org/10.1007/978-3-319-67558-9_28 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | artificial intelligence, classification, classification accuracy, computer architecture, computer vision, computerized tomography, graph theory, image analysis, image processing, image reconstruction, image registration, image segmentation, learning algorithms, learning systems, mammography, medical imaging, neural networks, segmentation methods, Support Vector Machines (SVM) |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Population Science and Experimental Medicine UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Population Science and Experimental Medicine > MRC Unit for Lifelong Hlth and Ageing UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1564359 |
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