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

Lesion images are frequently taken in open-set settings. Because of this, theimage data generated is extremely varied in nature.It is difficult for aconvolutional neural network to find proper features and generalise well, as aresult content based image retrieval (CBIR) system for lesion images aredifficult to build. This paper explores this domain and proposes multiplesimilarity measures which uses Style Loss and Dice Coefficient via a novelsimilarity measure called I1-Score. Out of the CBIR similarity measuresproposed, pure style loss approach achieves a remarkable accuracy increase overtraditional approaches like Euclidean Distance and Cosine Similarity. TheI1-Scores using style loss performed better than traditional approaches by asmall margin, whereas, I1-Scores with dice-coefficient faired very poorly. Themodel used is trained using ensemble learning for better generalization.

Exploring Content Based Image Retrieval for Highly Imbalanced Melanoma Data using Style Transfer, Semantic Image Segmentation and Ensemble Learning