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Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation Using Holistic Convolutional Networks

Fidon, L; Li, W; García-Peraza-Herrera, LC; Ekanayake, J; Kitchen, N; Ourselin, S; Vercauteren, T; (2018) Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation Using Holistic Convolutional Networks. In: Crimi, A and Bakas, S and Kuijf, HJ and Menze, BH and Reyes, M, (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. (pp. pp. 64-76). Springer (In press). Green open access

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Type: Proceedings paper
Title: Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation Using Holistic Convolutional Networks
Event: Third International Workshop (BrainLes 2017)
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-75238-9
Publisher version: https://doi.org/10.1007/978-3-319-75238-9
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.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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/10122153
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