earticle

영어

Colorectal cancer is one of the most common cancers worldwide and poses a significant threat to humans, and it is preventable through early detection of polyps. Consequently, research is actively underway to improve the performance of early detection using artificial intelligence, particularly through segmentation tasks. This segmentation task requires expert annotated images of lesion areas and medical images, requiring large amounts of high-quality data. However, due to the limitations of medical data, there are constraints in obtaining high-quality data. Augmentation by image generation models has been actively researched to address this issue. In this study, we propose a diffusion model that generates diverse images by strongly applying different weights to the lesion area and the background area of the annotation, thereby improving the diverse generation capability in the background area. The evaluation involved assessing the quality of the generated images, augmenting the original dataset with this generated data, and applying it to the segmentation task to evaluate the performance improvement. This proves that assigning varying weights to the background can address the issue of data scarcity in medical imaging.