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

Cardiac signals, such as the electrocardiogram, convey a significant amountof information about the health status of a patient which is typicallysummarized by a clinician in the form of a clinical report, a cumbersomeprocess that is prone to errors. To streamline this routine process, we proposea deep neural network capable of captioning cardiac signals; it receives acardiac signal as input and generates a clinical report as output. We extendthis further to generate multilingual reports. To that end, we create and makepublicly available a multilingual clinical report dataset. In the absence ofsufficient labelled data, deep neural networks can benefit from a warm-start,or pre-training, procedure in which parameters are first learned in anarbitrary task. We propose such a task in the form of discriminativemultilingual pre-training where tokens from clinical reports are randomlyreplaced with those from other languages and the network is tasked withpredicting the language of all tokens. We show that our method performs on parwith state-of-the-art pre-training methods such as MLM, ELECTRA, and MARGE,while simultaneously generating diverse and plausible clinical reports. We alsodemonstrate that multilingual models can outperform their monolingualcounterparts, informally terming this beneficial phenomenon as the blessing ofmultilinguality.

Let Your Heart Speak in its Mother Tongue: Multilingual Captioning of Cardiac Signals