keys string | pids string | lung_bboxes list | labels int64 | pixel_size list | data_path string | npy_path string | clinical_data dict | answer_dict dict | questions list | data_name string |
|---|---|---|---|---|---|---|---|---|---|---|
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1.2.840.113654.2.55.18656159377196234615117488653064966494 | 100020 | [
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1.2.840.113654.2.55.83924433894155499396855796981765658161 | 100020 | [
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"Is there any significant cardiovascular abnormality?",
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] | datasets/NLST/set2/batch7/100362/T0/1.2.840.113654.2.55.78099579994375626962896178258130017997 | datasets/NLST/npy/set2/batch7/100362/T0/1.2.840.113654.2.55.78099579994375626962896178258130017997.npy | {
"demo": {
"age": 64,
"educat": "Graduate School",
"ethnic": "Neither Hispanic nor Latino",
"gender": "Female",
"height": 62,
"race": "White",
"weight": 95
},
"smoking": {
"age_quit": -1,
"cigar": "No",
"cigsmok": "Current",
"pipe": "No",
"pkyr": 39,
"smokeage"... | {
"0": "No",
"1": "Yes"
} | [
"Is there any significant cardiovascular abnormality?",
"Can you identify any notable abnormalities in the cardiovascular system?",
"Are there any evident issues related to the cardiovascular system in the findings?",
"Are there discernible cardiovascular defects present?",
"Do you notice any cardiovascular... | CVD_diagnosis |
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OpenM3Chest Labels (OM3C)
JSON label files and Series UIDs from the OpenM3Chest dataset, prepared for fine-tuning medical vision-language models such as MedGemma.
Raw imaging data (DICOM) can be downloaded from IDC (Imaging Data Commons) using the Series Instance UIDs provided in unique_keys.txt.
Dataset Summary
OpenM3Chest is a medical multimodal multitask dataset for diagnosing chest abnormalities with a focus on lung cancer screening. The original raw data comes from NLST (National Lung Screening Trial) and MIDRC.
This repository contains the label files only (JSON format). Each sample includes:
keys— DICOM Series Instance UID (use with IDC to download imaging data)pids— Patient IDlung_bboxes— Bounding boxes for left/right lung regionslabels— Ground truth label (0 = No, 1 = Yes)pixel_size— Voxel spacingclinical_data— Patient demographics and clinical history (age, smoking, etc.)questions— List of natural language questions for VQA tasksdata_name— Task identifier
Tasks / Configs
| Config | Description |
|---|---|
chest_abn_54 |
Chest abnormality: Atelectasis (segmental or greater) |
chest_abn_55 |
Chest abnormality: Pleural thickening or effusion |
chest_abn_56 |
Chest abnormality: Type 56 |
chest_abn_57 |
Chest abnormality: Type 57 |
chest_abn_58 |
Chest abnormality: Type 58 |
chest_abn_59 |
Chest abnormality: Type 59 |
chest_abn_61 |
Chest abnormality: Type 61 |
CVD_diagnosis |
Cardiovascular disease diagnosis |
CVD_mortality |
Cardiovascular disease mortality prediction |
lung_cancer_risk |
Lung cancer risk assessment |
nodule_attenuation |
Lung nodule attenuation classification |
nodule_location |
Lung nodule location classification |
nodule_margin |
Lung nodule margin classification |
nodule_presence |
Lung nodule presence detection |
nodule_size |
Lung nodule size estimation |
Usage
from datasets import load_dataset
# Load a specific task
ds = load_dataset("UngLong/openm3chest-labels", name="chest_abn_54")
print(ds["train"][0])
print(ds["test"][0])
Download DICOM Images
Use the keys field (DICOM Series Instance UID) to download imaging data from IDC:
from idc_index import IDCClient
client = IDCClient()
series_uid = "1.2.840.113654.2.55.229650531101716203536241646069123704792"
client.download_dicom_series(seriesInstanceUID=series_uid, downloadDir="./dicom")
All 116,488 unique Series Instance UIDs are listed in unique_keys.txt.
Citation
If you use this dataset, please cite the original OpenM3Chest paper and dataset:
@dataset{openm3chest,
author = {Niu, Chuang},
title = {OpenM3Chest},
year = {2024},
doi = {10.5281/zenodo.14363994},
url = {https://zenodo.org/records/14363994}
}
License
MIT — see LICENSE for details. Original data sourced from NLST and MIDRC; please refer to their respective data use agreements.
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