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

Breast Cancer (BC) is among women's most lethal health concerns. Earlydiagnosis can alleviate the mortality rate by helping patients make efficienttreatment decisions. Human Epidermal Growth Factor Receptor (HER2) has becomeone the most lethal subtype of BC. According to the College of AmericanPathologists American Society of Clinical Oncology (CAP/ASCO), the severitylevel of HER2 expression can be classified between 0 and 3+ range. HER2 can bedetected effectively from immunohistochemical (IHC) and, hematoxylin & eosin(HE) images of different classes such as 0, 1+, 2+, and 3+. An ensembleapproach integrated with threshold filtered single instance evaluation (SIE)technique has been proposed in this study to diagnose BC from themulti-categorical expression of HER2 subtypes. Initially, DenseNet201 andXception have been ensembled into a single classifier as feature extractorswith an effective combination of global average pooling, dropout layer, denselayer with a swish activation function, and l2 regularizer, batchnormalization, etc. After that, extracted features has been processed throughsingle instance evaluation (SIE) to determine different confidence levels andadjust decision boundary among the imbalanced classes. This study has beenconducted on the BC immunohistochemical (BCI) dataset, which is classified bypathologists into four stages of HER2 BC. This proposed approach known asDenseNet201-Xception-SIE with a threshold value of 0.7 surpassed all otherexisting state-of-art models with an accuracy of 97.12%, precision of 97.15%,and recall of 97.68% on H&E data and, accuracy of 97.56%, precision of 97.57%,and recall of 98.00% on IHC data respectively, maintaining momentousimprovement. Finally, Grad-CAM and Guided Grad-CAM have been employed in thisstudy to interpret, how TL-based model works on the histopathology dataset andmake decisions from the data.

Addressing Uncertainty in Imbalanced Histopathology Image Classification  of HER2 Breast Cancer: An interpretable Ensemble Approach with Threshold  Filtered Single Instance Evaluation (SIE)