CONFLUX: A Latent Diusion Model for 3D Chest-CT Synthesis with RL Post-Training
Summary
CONFLUX is a 3D latent diffusion model for chest CT synthesis that achieves high-fidelity volumetric generation with controllable clinical attributes, enhanced by a reinforcement learning post-training stage to improve conditioning reliability. The model and a synthetic dataset of ~200k chest CT volumes are released.
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Paper page - CONFLUX: A Latent Diusion Model for 3D Chest-CT Synthesis with RL Post-Training
Source: https://huggingface.co/papers/2607.02998
Abstract
A 3D latent diffusion model for chest CT generation that achieves high-fidelity results while enabling control over clinical attributes through metadata conditioning and reinforcement learning refinement.
Controllable generative models of 3D medical images can synthesize volumes with specifiedclinical attributes, but this demands samples that are simultaneously high-fidelity, natively 3D, and faithful to the requested conditioning. We present CONFLUX, alatent diffusion modelforchest computed tomography(CT): a3D variational autoencodercompresses each volume, and arectified-flow transformergenerates in the latent space. Generation is conditioned on structuredradiological metadata(18 abnormality findings, sex, age, and reconstruction kernel) throughadaptive layer normalization. The model leads strong volumetric baselines ontri-planar Frechet distance(FID32.3 vs. 74.6 for MAISI) while exposing direct control overclinical attributes. To strengthen that control we add an onlinereinforcement-learning post-trainingstage (group-relative policy optimization) that rewards how reliably a classifier recovers the requested findings from each generated volume. Judged by a separate, independent classifier, post-training removes 47% of the shortfall relative to real-scan reliability. We release the model and a ~200k synthetic chest-CT dataset with conditioning metadata spanning a wide variety of clinical findings.
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#### gevaertlab/conflux Unconditional Image Generation• Updatedabout 4 hours ago • 9
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