Omkar2118/datasets huggingface dataset:
PhoNER COVID19

PhoNER_COVID19 is a dataset for recognising COVID-19 related named entities in Vietnamese, consisting of 35K entities over 10K sentences. The authors defined 10 entity types with the aim of extracting key information related to COVID-19 patients, which are especially useful in downstream applications. In general, these entity types can be used in the context of not only the COVID-19 pandemic but also in other future epidemics.

Introducing paper: COVID-19 Named Entity Recognition for Vietnamese
CAMUS
Full name: Cardiac Acquisitions for Multi-structure Ultrasound Segmentation

This project aims to provide all the materials to the community to resolve the problem of echocardiographic image segmentation and volume estimation from 2D ultrasound sequences (both two and four-chamber views). To this aim, the following solutions were set up.

1. Introduction of the largest publicly-available and fully-annotated dataset for 2D echocardiographic assessment (to our knowledge). The CAMUS dataset, containing 2D apical four-chamber and two-chamber view sequences acquired from 500 patients, is made available for download.

2. Deployment of a dedicated Girder online platform. This platform aims to assess in a reproducible manner the performance of methods for segmenting cardiac structures (left ventricle endocardium and epicardium and left atrium borders) and extracting clinical indices (left ventricle volumes and ejection fraction).

The CAMUS online platform is now available and will be maintained and kept open as long as the data remains relevant for clinical research.

Introducing paper: Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography