The invention relates to the crossing field of medical
artificial intelligence and
computer vision, and discloses a colonoscope polyp
image generation method based on a bidirectional information constraint
diffusion model, and the method comprises the steps: constructing and training a
noise prediction network based on conditional
mask constraint and dynamic reweighting loss; the colon polyp
mirror image generating module is used for generating a colon polyp
mirror image of a corresponding polyp tissue with the quality meeting the requirement under the guidance of the condition
mask; executing a sampling process based on bidirectional information constraint, dynamically combining a foreground generation image and a real background constraint image, inputting a condition
mask to the
noise prediction network, and gradually synthesizing a colon polyp
mirror image containing a real enteric cavity structure and a condition polyp tissue; and executing a sampling process based on bidirectional information constraint, dynamically combining a foreground generation image and a real background constraint image, inputting a condition mask to the
noise prediction network, and gradually synthesizing a colon polyp mirror image containing a real enteric cavity structure and a condition polyp tissue. The performance of the segmentation model can be effectively improved.