Conference paper · Preprint article · Book chapter
Semi-supervised variational autoencoder for survival prediction
Department of Health Technology, Technical University of Denmark1
Medical Image Computing, Biomedical Engineering, Department of Health Technology, Technical University of Denmark2
Biomedical Engineering, Department of Health Technology, Technical University of Denmark3
Department of Applied Mathematics and Computer Science, Technical University of Denmark4
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark5
In this paper we propose a semi-supervised variational autoencoder for classification of overall survival groups from tumor segmentation masks. The model can use the output of any tumor segmentation algorithm, removing all assumptions on the scanning platform and the specific type of pulse sequences used, thereby increasing its generalization properties.
Due to its semi-supervised nature, the method can learn to classify survival time by using a relatively small number of labeled subjects. We validate our model on the publicly available dataset from the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2019.
Language: | English |
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Publisher: | Springer |
Year: | 2020 |
Pages: | 124-134 |
Proceedings: | 5th International MICCAI Brainlesion Workshop, held in conjunction with the Medical Image Computing for Computer Assisted Intervention |
Series: | Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Journal subtitle: | 5th International Workshop, Brainles 2019, Held in Conjunction With Miccai 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part II |
ISBN: | 3030466426 , 3030466434 , 9783030466428 and 9783030466435 |
ISSN: | 03029743 and 16113349 |
Types: | Conference paper , Preprint article and Book chapter |
DOI: | 10.1007/978-3-030-46643-5_12 |
ORCIDs: | Cerri, Stefano , Dittadi, Andrea and Leemput, Koen Van |