Boosting(机器学习)
计算机科学
分割
人工智能
生成语法
生成模型
图像质量
计算机视觉
图像(数学)
模式识别(心理学)
作者
Shiwei Du,Xiaosong Wang,Yongyi Lu,Yuyin Zhou,Shaoting Zhang,Alan Yuille,Kang Li,Zongwei Zhou
出处
期刊:Cornell University - arXiv
日期:2023-10-04
标识
DOI:10.48550/arxiv.2310.02906
摘要
Image synthesis approaches, e.g., generative adversarial networks, have been popular as a form of data augmentation in medical image analysis tasks. It is primarily beneficial to overcome the shortage of publicly accessible data and associated quality annotations. However, the current techniques often lack control over the detailed contents in generated images, e.g., the type of disease patterns, the location of lesions, and attributes of the diagnosis. In this work, we adapt the latest advance in the generative model, i.e., the diffusion model, with the added control flow using lesion-specific visual and textual prompts for generating dermatoscopic images. We further demonstrate the advantage of our diffusion model-based framework over the classical generation models in both the image quality and boosting the segmentation performance on skin lesions. It can achieve a 9% increase in the SSIM image quality measure and an over 5% increase in Dice coefficients over the prior arts.
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