  Recent advances in text-conditioned image generation diffusion models have
begun paving the way for new opportunities in modern medical domain, in
particular, generating Chest X-rays (CXRs) from diagnostic reports.
Nonetheless, to further drive the diffusion models to generate CXRs that
faithfully reflect the complexity and diversity of real data, it has become
evident that a nontrivial learning approach is needed. In light of this, we
propose CXRL, a framework motivated by the potential of reinforcement learning
(RL). Specifically, we integrate a policy gradient RL approach with
well-designed multiple distinctive CXR-domain specific reward models. This
approach guides the diffusion denoising trajectory, achieving precise CXR
posture and pathological details. Here, considering the complex medical image
environment, we present "RL with Comparative Feedback" (RLCF) for the reward
mechanism, a human-like comparative evaluation that is known to be more
effective and reliable in complex scenarios compared to direct evaluation. Our
CXRL framework includes jointly optimizing learnable adaptive condition
embeddings (ACE) and the image generator, enabling the model to produce more
accurate and higher perceptual CXR quality. Our extensive evaluation of the
MIMIC-CXR-JPG dataset demonstrates the effectiveness of our RL-based tuning
approach. Consequently, our CXRL generates pathologically realistic CXRs,
establishing a new standard for generating CXRs with high fidelity to
real-world clinical scenarios.
