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 is one of the most common types of cancer and leadingcancer-related death causes for women. In the context of ICIAR 2018 GrandChallenge on Breast Cancer Histology Images, we compare one handcrafted featureextractor and five transfer learning feature extractors based on deep learning.We find out that the deep learning networks pretrained on ImageNet have betterperformance than the popular handcrafted features used for breast cancerhistology images. The best feature extractor achieves an average accuracy of79.30%. To improve the classification performance, a random forestdissimilarity based integration method is used to combine different featuregroups together. When the five deep learning feature groups are combined, theaverage accuracy is improved to 82.90% (best accuracy 85.00%). When handcraftedfeatures are combined with the five deep learning feature groups, the averageaccuracy is improved to 87.10% (best accuracy 93.00%).

Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images