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

The task of multimodal cancer detection is to determine the locations andcategories of lesions by using different imaging techniques, which is one ofthe key research methods for cancer diagnosis. Recently, deep learning-basedobject detection has made significant developments due to its strength insemantic feature extraction and nonlinear function fitting. However, multimodalcancer detection remains challenging due to morphological differences inlesions, interpatient variability, difficulty in annotation, and imagingartifacts. In this survey, we mainly investigate over 150 papers in recentyears with respect to multimodal cancer detection using deep learning, with afocus on datasets and solutions to various challenges such as data annotation,variance between classes, small-scale lesions, and occlusion. We also providean overview of the advantages and drawbacks of each approach. Finally, wediscuss the current scope of work and provide directions for the futuredevelopment of multimodal cancer detection.

Survey on deep learning in multimodal medical imaging for cancer detection