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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Brain tumour segmentation plays a key role in computer-assisted surgery. Deepneural networks have increased the accuracy of automatic segmentationsignificantly, however these models tend to generalise poorly to differentimaging modalities than those for which they have been designed, therebylimiting their applications. For example, a network architecture initiallydesigned for brain parcellation of monomodal T1 MRI can not be easilytranslated into an efficient tumour segmentation network that jointly utilisesT1, T1c, Flair and T2 MRI. To tackle this, we propose a novel scalablemultimodal deep learning architecture using new nested structures thatexplicitly leverage deep features within or across modalities. This aims atmaking the early layers of the architecture structured and sparse so that thefinal architecture becomes scalable to the number of modalities. We evaluatethe scalable architecture for brain tumour segmentation and give evidence ofits regularisation effect compared to the conventional concatenation approach.

Scalable multimodal convolutional networks for brain tumour segmentation