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

Histopathological images (HI) encrypt resolution dependent heterogeneoustextures & diverse color distribution variability, manifesting inmicro-structural surface tissue convolutions. Also, inherently high coherencyof cancerous cells poses significant challenges to breast cancer (BC)multi-classification. As such, multi-class stratification is sparsely explored& prior work mainly focus on benign & malignant tissue characterization only,which forestalls further quantitative analysis of subordinate classes likeadenosis, mucinous carcinoma & fibroadenoma etc, for diagnostic competence. Inthis work, a fully-automated, near-real-time & computationally inexpensiverobust multi-classification deep framework from HI is presented.  The proposed scheme employs deep neural network (DNN) aided discriminativeensemble of holistic class-specific manifold learning (CSML) for underlying HIsub-space embedding & HI hashing based local shallow signatures. The modelachieves 95.8% accuracy pertinent to multi-classification & 2.8% overallperformance improvement & 38.2% enhancement for Lobular carcinoma (LC)sub-class recognition rate as compared to the existing state-of-the-art on wellknown BreakHis dataset is achieved. Also, 99.3% recognition rate at 200X & asensitivity of 100% for binary grading at all magnification validates itssuitability for clinical deployment in hand-held smart devices.

Diving Deep onto Discriminative Ensemble of Histological Hashing & Class-Specific Manifold Learning for Multi-class Breast Carcinoma Taxonomy