ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic
  Diffusion Models
Yuhao Du Yuncheng Jiang Shuangyi Tan Xusheng Wu Qi Dou Zhen Li Guanbin Li Xiang Wan
Colonoscopy analysis, particularly automatic polyp segmentation and
detection, is essential for assisting clinical diagnosis and treatment.
However, as medical image annotation is labour- and resource-intensive, the
scarcity of annotated data limits the effectiveness and generalization of
existing methods. Although recent research has focused on data generation and
augmentation to address this issue, the quality of the generated data remains a
challenge, which limits the contribution to the performance of subsequent
tasks. Inspired by the superiority of diffusion models in fitting data
distributions and generating high-quality data, in this paper, we propose an
Adaptive Refinement Semantic Diffusion Model (ArSDM) to generate colonoscopy
images that benefit the downstream tasks. Specifically, ArSDM utilizes the
ground-truth segmentation mask as a prior condition during training and adjusts
the diffusion loss for each input according to the polyp/background size ratio.
Furthermore, ArSDM incorporates a pre-trained segmentation model to refine the
training process by reducing the difference between the ground-truth mask and
the prediction mask. Extensive experiments on segmentation and detection tasks
demonstrate the generated data by ArSDM could significantly boost the
performance of baseline methods.