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

This technical report presents a comparative analysis of existing deeplearning (DL) based approaches for brain tumor segmentation with missing MRImodalities. Approaches evaluated include the Adversarial Co-training Network(ACN) and a combination of mmGAN and DeepMedic. A more stable and easy-to-useversion of mmGAN is also open-sourced at a GitHub repository. Using theBraTS2018 dataset, this work demonstrates that the state-of-the-art ACNperforms better especially when T1c is missing. While a simple combination ofmmGAN and DeepMedic also shows strong potentials when only one MRI modality ismissing. Additionally, this work initiated discussions with future researchdirections for brain tumor segmentation with missing MRI modalities.

Analyzing Deep Learning Based Brain Tumor Segmentation with Missing MRI Modalities