Semi-supervised variational autoencoder for survival prediction

Sveinn Pálsson*, Stefano Cerri, Andrea Dittadi, Koen Van Leemput

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

31 Downloads (Pure)

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.

Original languageEnglish
Title of host publicationBrainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
EditorsAlessandro Crimi, Spyridon Bakas
PublisherSpringer
Publication date1 Jan 2020
Pages124-134
ISBN (Print)9783030466428
DOIs
Publication statusPublished - 1 Jan 2020
Event5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 17 Oct 201917 Oct 2019

Conference

Conference5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period17/10/201917/10/2019
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11993 LNCS
ISSN0302-9743

Keywords

  • Deep generative models
  • Semi-supervised VAE
  • Survival time

Fingerprint Dive into the research topics of 'Semi-supervised variational autoencoder for survival prediction'. Together they form a unique fingerprint.

Cite this