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

A shortage of dermatologists causes long wait times for patients who seekdermatologic care. In addition, the diagnostic accuracy of generalpractitioners has been reported to be lower than the accuracy of artificialintelligence software. This article describes the Skincare project (H2020, EITDigital). Contributions include enabling technology for clinical decisionsupport based on interactive machine learning (IML), a reference architecturetowards a Digital European Healthcare Infrastructure (also cf. EIT MCPS),technical components for aggregating digitised patient information, and theintegration of decision support technology into clinical test-bed environments.However, the main contribution is a diagnostic and decision support system indermatology for patients and doctors, an interactive deep learning system fordifferential diagnosis of malignant skin lesions. In this article, we describeits functionalities and the user interfaces to facilitate machine learning fromhuman input. The baseline deep learning system, which delivers state-of-the-artresults and the potential to augment general practitioners and evendermatologists, was developed and validated using de-identified cases from adermatology image data base (ISIC), which has about 20000 cases for developmentand validation, provided by board-certified dermatologists defining thereference standard for every case. ISIC allows for differential diagnosis, aranked list of eight diagnoses, that is used to plan treatments in the commonsetting of diagnostic ambiguity. We give an overall description of the outcomeof the Skincare project, and we focus on the steps to support communication andcoordination between humans and machine in IML. This is an integral part of thedevelopment of future cognitive assistants in the medical domain, and wedescribe the necessary intelligent user interfaces.

The Skincare project, an interactive deep learning system for differential diagnosis of malignant skin lesions. Technical Report