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

Accurate characterization of microcalcifications (MCs) in 2D full-fielddigital screening mammography is a necessary step towards reducing diagnosticuncertainty associated with the callback of women with suspicious MCs.Quantitative analysis of MCs has the potential to better identify MCs that havea higher likelihood of corresponding to invasive cancer. However, automatedidentification and segmentation of MCs remains a challenging task with highfalse positive rates. We present Hessian Difference of Gaussians Regression(HDoGReg), a two stage multi-scale approach to MC segmentation. Candidate highoptical density objects are first delineated using blob detection and Hessiananalysis. A regression convolutional network, trained to output a function withhigher response near MCs, chooses the objects which constitute actual MCs. Themethod is trained and validated on 435 mammograms from two separate datasets.HDoGReg achieved a mean intersection over the union of 0.670$\pm$0.121 perimage, intersection over the union per MC object of 0.607$\pm$0.250 and truepositive rate of 0.744 at 0.4 false positive detections per $cm^2$. The resultsof HDoGReg perform better when compared to state-of-the-art MC segmentation anddetection methods.

Segmentation of Breast Microcalcifications: A Multi-Scale Approach