Multi-label pathology editing of chest X-rays with a Controlled Diffusion Model

Abstract

Large-scale generative models have garnered significant attention in the field of medical imaging, particularly for image editing utilizing diffusion models. However, current research has predominantly concentrated on pathological editing involving single or a limited number of labels, making it challenging to achieve precise modifications. Inaccurate alterations may lead to substantial discrepancies between the generated and original images, thereby impacting the clinical applicability of these models. This paper presents a diffusion model with untangling capabilities applied to chest X-ray image editing, incorporating a mask-based mechanism for bone and organ information. We successfully perform multi-label pathological editing of chest X-ray images without compromising the integrity of the original thoracic structure. The proposed technology comprises a chest X-ray image classifier and an intricate organ mask; the classifier supplies essential feature labels that require untangling for the stabilized diffusion model, while the complex organ mask facilitates directed and controllable edits to chest X-rays. We assessed the outcomes of our proposed algorithm, named Chest X-rays_Mpe, using MS-SSIM and CLIP scores alongside qualitative evaluations conducted by radiology experts. The results indicate that our approach surpasses existing algorithms across both quantitative and qualitative metrics.