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

Inspired by the recent success of Transformers for Natural LanguageProcessing and vision Transformer for Computer Vision, many researchers in themedical imaging community have flocked to Transformer-based networks forvarious main stream medical tasks such as classification, segmentation, andestimation. In this study, we analyze, two recently published Transformer-basednetwork architectures for the task of multimodal head-and-tumor segmentationand compare their performance to the de facto standard 3D segmentation network- the nnU-Net. Our results showed that modeling long-range dependencies may behelpful in cases where large structures are present and/or large field of viewis needed. However, for small structures such as head-and-neck tumor, theconvolution-based U-Net architecture seemed to perform well, especially whentraining dataset is small and computational resource is limited.

Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation