Cov-caldas: A new COVID-19 chest X-Ray dataset from state of Caldas-Colombia.

The emergence of COVID-19 as a global pandemic forced researchers worldwide in various disciplines to investigate and propose efficient strategies and/or technologies to prevent COVID-19 from further spreading. One of the main challenges to be overcome is the fast and efficient detection of COVID-19 using deep learning approaches and medical images such as Chest Computed Tomography (CT) and Chest X-ray images. In order to contribute to this challenge, a new dataset was collected in collaboration with "S.E.S Hospital Universitario de Caldas" ( https://hospitaldecaldas.com/ ) from Colombia and organized following the Medical Imaging Data Structure (MIDS) format. The dataset contains 7,307 chest X-ray images divided into 3,077 and 4,230 COVID-19 positive and negative images. Images were subjected to a selection and anonymization process to allow the scientific community to use them freely. Finally, different convolutional neural networks were used to perform technical validation. This dataset contributes to the scientific community by tackling significant limitations regarding data quality and availability for the detection of COVID-19.

Beyond the Brain: MIDS Extends BIDS to Multiple Modalities and Anatomical Regions.

Brain Imaging Data Structure (BIDS) provides a valuable tool to organise brain imaging data into a clear and easy standard directory structure. Moreover, BIDS is widely supported by the scientific community and has been established as a powerful standard for medical imaging management. Nonetheless, the original BIDS is restricted to magnetic resonance imaging (MRI) of the brain, limiting its implantation to other techniques and anatomical regions. We developed Medical Imaging Data Structure (MIDS), conceived to extend BIDS methodology to other anatomical regions and multiple imaging systems in these areas. The MIDS standard was developed to store and manage medical images as an extension of BIDS. It allows the user to handily save studies of multiple anatomical regions and imaging techniques. Besides, MIDS improves the classification of multiple images within the structure, allowing the possibility to unify them in a single study to apply on them preprocessing or artificial intelligence algorithms. Finally, the results generated are saved in the derivatives folder.

Beyond the Brain: MIDS Extends BIDS to Multiple Modalities and Anatomical Regions.

Brain Imaging Data Structure (BIDS) provides a valuable tool to organise brain imaging data into a clear and easy standard directory structure. Moreover, BIDS is widely supported by the scientific community and has been established as a powerful standard for medical imaging management. Nonetheless, the original BIDS is restricted to magnetic resonance imaging (MRI) of the brain, limiting its implantation to other techniques and anatomical regions. We developed Medical Imaging Data Structure (MIDS), conceived to extend BIDS methodology to other anatomical regions and multiple imaging systems in these areas. The MIDS standard was developed to store and manage medical images as an extension of BIDS. It allows the user to handily save studies of multiple anatomical regions and imaging techniques. Besides, MIDS improves the classification of multiple images within the structure, allowing the possibility to unify them in a single study to apply on them preprocessing or artificial intelligence algorithms. Finally, the results generated are saved in the derivatives folder.