Datasets:
metadata
dataset_info:
- config_name: findings_section
features:
- name: dicom_id
dtype: string
- name: study_id
dtype: string
- name: subject_id
dtype: string
- name: main_image
dtype: image
- name: findings_section
dtype: string
- name: impression_section
dtype: string
- name: indication_section
dtype: string
- name: comparison_section
dtype: string
- name: technique_section
dtype: string
- name: history_section
dtype: string
- name: examination_section
dtype: string
- name: prior_image
dtype: image
- name: default_prompt
dtype: string
- name: PerformedProcedureStepDescription
dtype: string
- name: ViewPosition
dtype: string
- name: Rows
dtype: int32
- name: Columns
dtype: int32
- name: StudyDate
dtype: string
- name: StudyTime
dtype: string
- name: ProcedureCodeSequence_CodeMeaning
dtype: string
- name: ViewCodeSequence_CodeMeaning
dtype: string
- name: PatientOrientationCodeSequence_CodeMeaning
dtype: string
- name: view
dtype: string
splits:
- name: test
num_bytes: 7996686675.113
num_examples: 2461
download_size: 7519081766
dataset_size: 7996686675.113
- config_name: impression_section
features:
- name: dicom_id
dtype: string
- name: study_id
dtype: string
- name: subject_id
dtype: string
- name: main_image
dtype: image
- name: findings_section
dtype: string
- name: impression_section
dtype: string
- name: indication_section
dtype: string
- name: comparison_section
dtype: string
- name: technique_section
dtype: string
- name: history_section
dtype: string
- name: examination_section
dtype: string
- name: prior_image
dtype: image
- name: default_prompt
dtype: string
- name: PerformedProcedureStepDescription
dtype: string
- name: ViewPosition
dtype: string
- name: Rows
dtype: int32
- name: Columns
dtype: int32
- name: StudyDate
dtype: string
- name: StudyTime
dtype: string
- name: ProcedureCodeSequence_CodeMeaning
dtype: string
- name: ViewCodeSequence_CodeMeaning
dtype: string
- name: PatientOrientationCodeSequence_CodeMeaning
dtype: string
- name: view
dtype: string
splits:
- name: test
num_bytes: 7553630749.877
num_examples: 2343
download_size: 7149201197
dataset_size: 7553630749.877
configs:
- config_name: findings_section
data_files:
- split: test
path: findings_section/test-*
- config_name: impression_section
data_files:
- split: test
path: impression_section/test-*
task_categories:
- image-text-to-text
language:
- en
tags:
- chest-xray
- report-generation
- mimic-cxr
pretty_name: MIMIC-CXR Radiology Report Generation
size_categories:
- 1K<n<10K
MIMIC-CXR-RRG: Radiology Report Generation Subsets
This dataset provides two carefully filtered and structured subsets from the MIMIC-CXR dataset, specifically designed for Radiology Report Generation (RRG) tasks. It includes image-report pairs focused on the Findings and Impression sections, targeting frontal-view chest X-rays only.
π Dataset Overview
Subset | Section Target | Split | #Samples | View Type |
---|---|---|---|---|
findings_section |
Findings | test | 2361 | Frontal only |
impression_section |
Impression | test | 2343 | Frontal only |
- The splits follow the evaluation protocol used in models such as Libra and MAIRA-2.
- Images and labels are provided in a test-only setting, useful for benchmarking and zero-shot evaluation.
π§Ύ Data Format
Each instance in both subsets contains:
- π·
main_image
β The frontal-view chest X-ray - π·
prior_image
β (Optional) Prior image if available - π Text sections:
findings_section
impression_section
indication_section
comparison_section
technique_section
history_section
examination_section
- π¬
default_prompt
β Prompt for generation tasks - π§Ύ Metadata:
dicom_id
,study_id
,subject_id
- Acquisition info:
Rows
,Columns
,StudyDate
,ViewPosition
, etc.
π How to Use
from datasets import load_dataset
# Load a specific subset (e.g., findings_section)
ds = load_dataset("X-iZhang/MIMIC-CXR-RRG", name="findings_section", split="test")
# Display an image
from PIL import Image
ds[0]["main_image"].show()
# View sample
print(ds[0]["findings_section"])
βοΈ Citation
@misc{zhang2025libraleveragingtemporalimages,
title={Libra: Leveraging Temporal Images for Biomedical Radiology Analysis},
author={Xi Zhang and Zaiqiao Meng and Jake Lever and Edmond S. L. Ho},
year={2025},
eprint={2411.19378},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2411.19378},
}