# BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients

arXiv: https://arxiv.org/abs/2006.01174 · HTML: https://mlsanity.com/paper/2006.01174/

## Key facts

- Cited by 82 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2020.

## Built on by (most cited first, 30 of 82)

- [GREEN: Generative Radiology Report Evaluation and Error Notation](https://mlsanity.com/paper/2405.03595/) (2024)
- [CheXpert Plus: Augmenting a Large Chest X-ray Dataset with Text Radiology Reports, Patient Demographics and Additional Image Formats](https://mlsanity.com/paper/2405.19538/) (2024)
- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [Full-Spectrum Out-of-Distribution Detection](https://mlsanity.com/paper/2204.05306/) (2022)
- [Concept-level Debugging of Part-Prototype Networks](https://mlsanity.com/paper/2205.15769/) (2022)
- [OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images](https://mlsanity.com/paper/2202.10185/) (2022)
- [Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging](https://mlsanity.com/paper/2205.08576/) (2022)
- [BIMCV-R: A Landmark Dataset for 3D CT Text-Image Retrieval](https://mlsanity.com/paper/2403.15992/) (2024)
- [RadEdit: stress-testing biomedical vision models via diffusion image editing](https://mlsanity.com/paper/2312.12865/) (2023)
- [Improving deep neural network generalization and robustness to background bias via layer-wise relevance propagation optimization](https://mlsanity.com/paper/2202.00232/) (2022)
- [COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Images](https://mlsanity.com/paper/2105.06640/) (2021)
- [Vision Transformers in Medical Computer Vision -- A Contemplative Retrospection](https://mlsanity.com/paper/2203.15269/) (2022)
- [Comprehensive language-image pre-training for 3D medical image understanding](https://mlsanity.com/paper/2510.15042/) (2025)
- [Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantification](https://mlsanity.com/paper/2104.07235/) (2021)
- [Overview of the First Shared Task on Clinical Text Generation: RRG24 and "Discharge Me!"](https://mlsanity.com/paper/2409.16603/) (2024)
- [Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training](https://mlsanity.com/paper/2111.01338/) (2021)
- [AI can evolve without labels: self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation](https://mlsanity.com/paper/2202.06431/) (2022)
- [A Comprehensive Survey on Evaluating Large Language Model Applications in the Medical Industry](https://mlsanity.com/paper/2404.15777/) (2024)
- [MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generation](https://mlsanity.com/paper/2409.19684/) (2024)
- [AdaNeg: Adaptive Negative Proxy Guided OOD Detection with Vision-Language Models](https://mlsanity.com/paper/2410.20149/) (2024)
- [A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron](https://mlsanity.com/paper/2202.06372/) (2022)
- [Vision Transformer for COVID-19 CXR Diagnosis using Chest X-ray Feature Corpus](https://mlsanity.com/paper/2103.07055/) (2021)
- [A foundation model for generalizable disease diagnosis in chest X-ray images](https://mlsanity.com/paper/2410.08861/) (2024)
- [Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutation](https://mlsanity.com/paper/2204.03500/) (2022)
- [COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray Images for Computer-Aided COVID-19 Diagnostics](https://mlsanity.com/paper/2206.03671/) (2022)
- [e-Health CSIRO at RRG24: Entropy-Augmented Self-Critical Sequence Training for Radiology Report Generation](https://mlsanity.com/paper/2408.03500/) (2024)
- [Recent Progress in Transformer-based Medical Image Analysis](https://mlsanity.com/paper/2208.06643/) (2022)
- [Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representations](https://mlsanity.com/paper/2203.01933/) (2022)
- [Vision Transformers in Medical Imaging: A Review](https://mlsanity.com/paper/2211.10043/) (2022)
- [SPCXR: Self-supervised Pretraining using Chest X-rays Towards a Domain Specific Foundation Model](https://mlsanity.com/paper/2211.12944/) (2022)

## Abstract (arXiv)

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.