XReal: Realistic Anatomy and Pathology-Aware X-ray Generation via
  Controllable Diffusion Model
Anees Ur Rehman Hashmi Ibrahim Almakky Mohammad Areeb Qazi Santosh Sanjeev Vijay Ram Papineni Dwarikanath Mahapatra Mohammad Yaqub
Large-scale generative models have demonstrated impressive capacity in
producing visually compelling images, with increasing applications in medical
imaging. However, they continue to grapple with the challenge of image
hallucination and the generation of anatomically inaccurate outputs. These
limitations are mainly due to the sole reliance on textual inputs and lack of
spatial control over the generated images, hindering the potential usefulness
of such models in real-life settings. We present XReal, a novel controllable
diffusion model for generating realistic chest X-ray images through precise
anatomy and pathology location control. Our lightweight method can seamlessly
integrate spatial control in a pre-trained text-to-image diffusion model
without fine-tuning, retaining its existing knowledge while enhancing its
generation capabilities. XReal outperforms state-of-the-art x-ray diffusion
models in quantitative and qualitative metrics while showing 13% and 10%
anatomy and pathology realism gain, respectively, based on the expert
radiologist evaluation. Our model holds promise for advancing generative models
in medical imaging, offering greater precision and adaptability while inviting
further exploration in this evolving field. A large synthetically generated
data with annotations and code is publicly available at
https://github.com/BioMedIA-MBZUAI/XReal.