Skin is the biggest organ in human being, and skin disease is one of the most common ones. Most people have skin-related problems. With the rapid development of computer and artificial intelligence, image-based methods for skin analysis have achieved preferable results and received increasing attention in academia and industry. 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, we propose a vision-based unified framework for dermatological analysis termed as parallel skin. Inspired by the ACP method and the parallel medical image analysis framework, we construct the artificial skin image system to perform data selection and generation. Then, computational experiments are conducted with predictive learning for model building and evaluation. We further introduce descriptive and prescriptive learning to leverage the power of domain knowledge to guide data selection and generation. In the proposed parallel-skin framework, the closed-loop diagnostic analysis model can be optimized.
|Translated title of the contribution||Parallel skin: a vision-based dermatological analysis framework|
|Number of pages||12|
|Journal||Journal of Pattern Recognition and Artificial Intelligence|
|Publication status||Published - 7 Jul 2019|
- parallel skin
- parallel intelligence
- generative models