---
license: cc-by-nc-sa-4.0
pretty_name: CONFLUX Chest-CT
tags:
  - medical
  - medical-imaging
  - computed-tomography
  - chest-ct
  - synthetic
  - 3d
task_categories:
  - image-classification
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    data_files: metadata.csv
extra_gated_prompt: >-
  CONFLUX Chest-CT is a synthetic dataset released for non-commercial research
  under CC BY-NC-SA 4.0. By requesting access you agree to research-only use and
  to cite the CONFLUX paper.
extra_gated_fields:
  Name: text
  Affiliation: text
  Intended use: text
  I will use this dataset for non-commercial research only: checkbox
---

![CONFLUX Chest-CT preview](preview.png)

# CONFLUX Chest-CT

**200,000 synthetic 3D chest CT volumes with structured abnormality and demographic labels**, generated by [CONFLUX](https://huggingface.co/gevaertlab/conflux).

Released with the paper **_CONFLUX: A Latent Diffusion Model for 3D Chest-CT Synthesis with RL Post-Training_**.

<p>
  <a href="https://arxiv.org/abs/2607.02998">Paper (arXiv)</a> &nbsp;•&nbsp;
  <a href="https://huggingface.co/gevaertlab/conflux">Model</a> &nbsp;•&nbsp;
  Code — <em>coming soon</em>
</p>

## About

CONFLUX is a conditional 3D latent generative model for chest CT: a VAE tokenizer
compresses each volume into a compact 16-channel latent, a single-stream
rectified-flow transformer generates in that latent space, and a reinforcement-learning
stage sharpens label faithfulness. Every volume in this release is generated from a
clinical profile — **18 abnormality findings, sex, age group, and reconstruction
kernel** — so the cohort is fully labeled and covers realistic clinical variation.
See the paper for the model, training, and evaluation.

| | |
| --- | --- |
| **Samples** | 200,000 volumes |
| **Modality / region** | CT / chest (lung-focused) |
| **Grid** | 216 × 176 × 200 voxels, 1.5 mm isotropic |
| **Format** | `int16` Hounsfield units (NIfTI) |
| **Labels** | 18 abnormality findings + sex + age group + reconstruction kernel |
| **License** | CC BY-NC-SA 4.0 (non-commercial research) |

## Dataset structure

```
conflux-chest-ct/
├── metadata.csv            one row per volume (schema below)
└── data/
    ├── 000/  sample_000000.nii.gz … sample_000999.nii.gz
    ├── 001/  …
    └── 199/  … sample_199999.nii.gz     (bucketed by index // 1000)
```

**`metadata.csv`** columns:

| column | description |
| --- | --- |
| `file_name` | relative path to the volume (join key) |
| `sex` | `M` / `F` |
| `age_group` | decade band, e.g. `60-69`, `70+` |
| `kernel` | reconstruction-kernel label |
| *18 finding columns* | binary presence of each abnormality |

Each `.nii.gz` is a `216 × 176 × 200` `int16` array in Hounsfield units; voxel
geometry is stored in the NIfTI affine.

## Usage

```python
from huggingface_hub import hf_hub_download
import pandas as pd, nibabel as nib

REPO = "gevaertlab/conflux-chest-ct"
meta = pd.read_csv(hf_hub_download(REPO, "metadata.csv", repo_type="dataset"))

# e.g. all cardiomegaly cases
cardio = meta[meta["Cardiomegaly"] == 1]
path = hf_hub_download(REPO, cardio.iloc[0]["file_name"], repo_type="dataset")
vol = nib.load(path).get_fdata()          # (216, 176, 200) int16 Hounsfield units
```

## Intended use

Research use — pre-training, augmentation, and benchmarking for chest-CT models
where real labeled data is scarce or access-restricted. **Not for clinical use.**
The volumes are synthetic: generated by a model, they do not correspond to real patients.
The cohort was screened for training-data memorization (no copies of training scans).

## Citation

```bibtex
@article{vanpuyvelde2026conflux,
  title   = {CONFLUX: A Latent Diffusion Model for 3D Chest-CT Synthesis with RL Post-Training},
  author  = {Van Puyvelde, Max and Gulluk, Halil Ibrahim and Van Criekinge, Wim and Gevaert, Olivier},
  journal = {arXiv preprint arXiv:2607.02998},
  year    = {2026}
}
```

## Acknowledgements

CONFLUX was trained on [CT-RATE](https://huggingface.co/datasets/ibrahimhamamci/CT-RATE) (Hamamci et al., 2024).

## License

CC BY-NC-SA 4.0 — non-commercial research use.