BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients
arXiv:2006.01174
Abstract
This paper describes BIMCV COVID-19+, a large dataset from the Valencian Region Medical ImageBank (BIMCV) containing chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19+ patients along with their radiological findings and locations, pathologies, radiological reports (in Spanish), DICOM metadata, Polymerase chain reaction (PCR), Immunoglobulin G (IgG) and Immunoglobulin M (IgM) diagnostic antibody tests. The findings have been mapped onto standard Unified Medical Language System (UMLS) terminology and cover a wide spectrum of thoracic entities, unlike the considerably more reduced number of entities annotated in previous datasets. Images are stored in high resolution and entities are localized with anatomical labels and stored in a Medical Imaging Data Structure (MIDS) format. In addition, 10 images were annotated by a team of radiologists to include semantic segmentation of radiological findings. This first iteration of the database includes 1,380 CX, 885 DX and 163 CT studies from 1,311 COVID-19+ patients. This is, to the best of our knowledge, the largest COVID-19+ dataset of images available in an open format. The dataset can be downloaded from http://bimcv.cipf.es/bimcv-projects/bimcv-covid19.
Cited by in corpus (12)
- Deep Learning for Chest X-ray Analysis: A Survey
- Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging
- Recent Progress in Transformer-based Medical Image Analysis
- OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images
- Improving deep neural network generalization and robustness to background bias via layer-wise relevance propagation optimization
- Ensembles of Convolutional Neural Networks models for pediatric pneumonia diagnosis
- Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
- GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for Identifying COVID-19 on Chest X-rays
- Clinical Insights: A Comprehensive Review of Language Models in Medicine
- Spectral decoupling allows training transferable neural networks in medical imaging
- R2C-GAN: Restore-to-Classify Generative Adversarial Networks for Blind X-Ray Restoration and COVID-19 Classification
- Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis