dnamodel/xraydar-multimodal

X-Raydar Multimodal Chest X-Ray Dataset

A multimodal dataset of 979 chest X-ray examinations, each with:

1. Chest X-ray image (full-resolution PNG, anonymised)
2. Consensus image-level labels (37 radiological findings, agreed by two expert radiologists with adjudication)
3. Bounding box annotations on the image from each annotator independently (localising each finding)
4. Original radiology report text
5. Report span annotations (token-level labels across 45 categories)

This dataset combines resources from two annotation processes described in the papers below. The image-level consensus labels were agreed by two experienced radiologists with a third senior radiologist adjudicating disagreements. Individual bounding boxes from each annotator are provided separately (identified by the annotator field), allowing researchers to study inter-annotator agreement or merge boxes as needed. The report text annotations were produced independently by a separate team of radiologists.

Website: x-raydar.info X-ray classifier: dnamodel/xraydar-cv · Code Report classifier: dnamodel/xraydar-nlp · Code Full annotated reports (29,756): dnamodel/xraydar-reports

Dataset Structure

images/                     # 979 chest X-ray PNG images (full resolution)
  {xray_id}.png
annotations.jsonl           # All annotations in one file

Annotation Format

Each line in annotations.jsonl is a JSON object:

{
  "xray_id": "1858",
  "image_file": "images/1858.png",
  "consensus_labels": ["consolidation", "mediastinum_widened", "unfolded_aorta", "volume_loss"],
  "bounding_boxes": [
    {"label": "consolidation", "annotator": "705", "x_min": 1200, "y_min": 800, "x_max": 1500, "y_max": 1100},
    {"label": "mediastinum_widened", "annotator": "703", "x_min": 700, "y_min": 200, "x_max": 1000, "y_max": 600}
  ],
  "report_text": "There is consolidation in the left lower zone...",
  "report_tokens": ["There", "is", "consolidation", "in", "the", "..."],
  "report_iobe_tags": ["B", "I", "I", "I", "I", "..."],
  "report_spans": [
    {"label": "consolidation", "start": 0, "end": 8}
  ],
  "report_labels": ["consolidation", "volume_loss"]
}

Annotation Sources

Field	Source	Description
consensus_labels	Two radiologists + adjudication	Agreed image-level findings (37 classes)
bounding_boxes	Two radiologists independently	Spatial localisation of findings, with annotator ID per box
report_text	Historical clinical report	Original free-text report written at time of exam
report_spans	Separate annotation team	Token-level finding labels extracted from the report text (45 classes)
report_labels	Derived from report_spans	Report-level labels (findings mentioned in text)

Note: The image annotations and report annotations were produced independently. Image labels reflect what is visible in the image; report labels reflect what was written by the original reporting radiologist. These do not necessarily agree — a finding may be visible but unreported, or mentioned in the report but not visually apparent.

Image-Level Finding Categories (37 classes)

#	Finding	#	Finding
0	Abnormal (non-clinically important)	19	Interstitial shadowing
1	Aortic calcification	20	Mediastinum displaced
2	Apical changes	21	Mediastinum widened
3	Atelectasis	22	Object
4	Axillary abnormality	23	Paraspinal mass
5	Bronchial changes	24	Paratracheal/hilar enlargement
6	Bulla	25	Parenchymal lesion
7	Cardiomegaly	26	Pleural abnormality
8	Cavity	27	Pleural effusion
9	Clavicle fracture	28	Pneumomediastinum
10	Consolidation	29	Pneumoperitoneum
11	Cardiac calcification	30	Pneumothorax
12	Dextrocardia	31	Rib fracture
13	Dilated bowel	32	Rib lesion
14	Emphysema	33	Scoliosis
15	Ground-glass opacification	34	Subcutaneous emphysema
16	Hemidiaphragm elevated	35	Tortuosity of aorta
17	Hernia	36	Pulmonary blood flow redistribution
18	Hyperexpanded lungs	37	Volume loss

Usage

from huggingface_hub import snapshot_download
import json
from PIL import Image

# Download dataset
snapshot_download(
    repo_id="dnamodel/xraydar-multimodal",
    repo_type="dataset",
    local_dir="./xraydar-multimodal"
)

# Load annotations
with open("./xraydar-multimodal/annotations.jsonl") as f:
    data = [json.loads(line) for line in f]

# Load an image with its annotations
exam = data[0]
img = Image.open(f"./xraydar-multimodal/{exam['image_file']}")
print(f"Image: {img.size}")
print(f"Consensus labels: {exam['consensus_labels']}")
print(f"Bounding boxes: {len(exam['bounding_boxes'])}")
print(f"Report: {exam['report_text'][:100]}...")

Data Provenance

• Images: Frontal chest X-rays from three UK NHS hospital networks (2006–2019), extracted from DICOM as anonymised PNGs at native resolution.
• Image annotations: Produced using the AnnotateX platform. Two experienced radiologists independently annotated each image with finding labels and bounding boxes. Image-level consensus labels were agreed through adjudication with a third senior radiologist. Individual bounding boxes from each annotator are preserved with their annotator IDs.
• Report text: Historical free-text radiology reports associated with each examination.
• Report annotations: 29,756 reports were manually annotated by ten radiologists using the AnnotateX platform, with span-level labels across 45 categories.

Citation

If you use this dataset, please cite:

@article{cid2024development,
  title={Development and validation of open-source deep neural networks for comprehensive chest
         x-ray reading: a retrospective, multicentre study},
  author={Cid, Yan Digilov and Macpherson, Matt and others},
  journal={The Lancet Digital Health},
  volume={6}, number={1}, pages={e44--e57},
  year={2024}, publisher={Elsevier},
  doi={10.1016/S2589-7500(23)00218-2}
}

@inproceedings{zhu2024multitask,
  title={A Multi-Task Transformer Model for Fine-grained Labelling of Chest {X}-Ray Reports},
  author={Zhu, Yuanyi and Liakata, Maria and Montana, Giovanni},
  booktitle={Proceedings of the 2024 Joint International Conference on Computational Linguistics,
             Language Resources and Evaluation (LREC-COLING 2024)},
  pages={862--875},
  year={2024},
  publisher={ELRA and ICCL}
}

License

For academic research and non-commercial evaluation only. See x-raydar.info for terms and conditions.

Contact

Giovanni Montana — g.montana@warwick.ac.uk