English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

With the rise in importance of personalized medicine, we trained personalizedneural networks to detect tumor progression in longitudinal datasets. The modelwas evaluated on two datasets with a total of 64 scans from 32 patientsdiagnosed with glioblastoma multiforme (GBM). Contrast-enhanced T1w sequencesof brain magnetic resonance imaging (MRI) images were used in this study. Foreach patient, we trained their own neural network using just two images fromdifferent timepoints. Our approach uses a Wasserstein-GAN (generativeadversarial network), an unsupervised network architecture, to map thedifferences between the two images. Using this map, the change in tumor volumecan be evaluated. Due to the combination of data augmentation and the networkarchitecture, co-registration of the two images is not needed. Furthermore, wedo not rely on any additional training data, (manual) annotations orpre-training neural networks. The model received an AUC-score of 0.87 for tumorchange. We also introduced a modified RANO criteria, for which an accuracy of66% can be achieved. We show that using data from just one patient can be usedto train deep neural networks to monitor tumor change.

'A net for everyone': fully personalized and unsupervised neural networks trained with longitudinal data from a single patient