@inproceedings{51b23f3301a04c7e8c4840dfbb838cc8,
title = "Semi-supervised variational autoencoder for survival prediction",
abstract = "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.",
keywords = "Deep generative models, Semi-supervised VAE, Survival time",
author = "Sveinn P{\'a}lsson and Stefano Cerri and Andrea Dittadi and Leemput, {Koen Van}",
year = "2020",
month = jan,
day = "1",
doi = "10.1007/978-3-030-46643-5_12",
language = "English",
isbn = "9783030466428",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "124--134",
editor = "Alessandro Crimi and Spyridon Bakas",
booktitle = "Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries",
note = "5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019 ; Conference date: 17-10-2019 Through 17-10-2019",
}