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

Infrared (IR) microscopes measure spectral information that quantifiesmolecular content to assign the identity of biomedical cells but lack thespatial quality of optical microscopy to appreciate morphologic features. Here,we propose a method to utilize the semantic information of cellular identityfrom IR imaging with the morphologic detail of pathology images in a deeplearning-based approach to image super-resolution. Using Generative AdversarialNetworks (GANs), we enhance the spatial detail in IR imaging beyond thediffraction limit while retaining their spectral contrast. This technique canbe rapidly integrated with modern IR microscopes to provide a framework usefulfor routine pathology.

A deep learning framework for morphologic detail beyond the diffraction limit in infrared spectroscopic imaging