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

This report describes our submission to the ISIC 2017 Challenge in SkinLesion Analysis Towards Melanoma Detection. We have participated in the Part 3:Lesion Classification with a system for automatic diagnosis of nevus, melanomaand seborrheic keratosis. Our approach aims to incorporate the expert knowledgeof dermatologists into the well known framework of Convolutional NeuralNetworks (CNN), which have shown impressive performance in many visualrecognition tasks. In particular, we have designed several networks providinglesion area identification, lesion segmentation into structural patterns andfinal diagnosis of clinical cases. Furthermore, novel blocks for CNNs have beendesigned to integrate this information with the diagnosis processing pipeline.

Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for the Diagnosis of Skin Lesions