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

Reliable large-scale cell detection and segmentation is the fundamental firststep to understanding biological processes in the brain. The ability tophenotype cells at scale can accelerate preclinical drug evaluation andsystem-level brain histology studies. The impressive advances in deep learningoffer a practical solution to cell image detection and segmentation.Unfortunately, categorizing cells and delineating their boundaries for trainingdeep networks is an expensive process that requires skilled biologists. Thispaper presents a novel self-supervised Dual-Loss Adaptive Masked Autoencoder(DAMA) for learning rich features from multiplexed immunofluorescence brainimages. DAMA's objective function minimizes the conditional entropy inpixel-level reconstruction and feature-level regression. Unlike existingself-supervised learning methods based on a random image masking strategy, DAMAemploys a novel adaptive mask sampling strategy to maximize mutual informationand effectively learn brain cell data. To the best of our knowledge, this isthe first effort to develop a self-supervised learning method for multiplexedimmunofluorescence brain images. Our extensive experiments demonstrate thatDAMA features enable superior cell detection, segmentation, and classificationperformance without requiring many annotations.

Multiplexed Immunofluorescence Brain Image Analysis Using Self-Supervised Dual-Loss Adaptive Masked Autoencoder