Abstract: self-supervised 3d context feature learning on unlabeled volume data

Maximilian Blendowski*, Mattias P. Heinrich

*Corresponding author for this work


Deep learning with convolutional networks (DCNN) has established itself as a powerful tool for a variety of medical imaging tasks. However, DCNNs in particular require strong monitoring by expert annotations, which cannot be generated cost-effectively by laymen. In contrast to manual annotations, the mere availability of medical volume data is not a problem.

Original languageEnglish
Title of host publicationBildverarbeitung für die Medizin 2020
EditorsThomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm
Number of pages1
PublisherSpringer Vieweg, Wiesbaden
Publication date12.02.2020
ISBN (Print)978-3-658-29266-9
ISBN (Electronic)978-3-658-29267-6
Publication statusPublished - 12.02.2020
EventBildverarbeitung für die Medizin 2020 - International workshop on Algorithmen - Systeme - Anwendungen
- Berlin, Germany
Duration: 15.03.202017.03.2020
Conference number: 237969


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