平行皮肤:基于视觉的皮肤病分析框架

Parallel Skin: A Vision-Based Dermatological Analysis Framework

随着计算机与人工智能的快速发展,基于图像感知的皮肤病分析方法取得一些成果.然而,以深度学习为主的计算机辅助分析方法依赖于领域专家标注的医学大数据,诊断结果缺乏医学可解释性.为此,文中提出基于视觉的皮肤病分析统一框架——平行皮肤.启发于ACP方法与平行医学图像分析框架,通过构建人工皮肤图像系统实现数据选择与生成,通过预测学习的计算实验完成诊断分析模型构建与评估,并利用描述学习与指示学习融合专家知识,引导人工图像系统数据生成与选择,从而实现闭环诊断分析模型优化.

With the rapid development of computer and artificial intelligence, image-based methods for skin analysis have achieved preferable results. However, the performance of computer aided diagnosis systems based on deep learning methods relies on big medical data labeled by domain experts. In addition, there is limitation of interpretability for the diagnosis results. To address aforementioned problems, a vision-based unified framework for dermatological analysis termed as parallel skin is proposed. Inspired by the ACP method and the parallel medical image analysis framework, the artificial skin image system to perform data selection and generation is constructed. Then, computational experiments are conducted with predictive learning for model building and evaluation. Descriptive and prescriptive learning to leverage the power of domain knowledge to guide data selection and generation are further introduced. In the proposed parallel-skin framework, the closed-loop diagnostic analysis model can be optimized.