This report describes our submission to the ISIC 2017 Challenge in Skin
Lesion Analysis Towards Melanoma Detection. We have participated in the Part 3:
Lesion Classification with a system for automatic diagnosis of nevus, melanoma
and seborrheic keratosis. Our approach aims to incorporate the expert knowledge
of dermatologists into the well known framework of Convolutional Neural
Networks (CNN), which have shown impressive performance in many visual
recognition tasks. In particular, we have designed several networks providing
lesion area identification, lesion segmentation into structural patterns and
final diagnosis of clinical cases. Furthermore, novel blocks for CNNs have been
designed to integrate this information with the diagnosis processing pipeline.

本文介绍了我们参加 ISIC 2017 挑战赛中皮肤病变分析与黑色素瘤检测的提交成果，我们的方法为卷积神经网络框架下结合多种分区和分类方法进行自动诊断，使得临床医师的专业知识得以应用。

利用皮肤科医生的知识将卷积神经网络应用于皮肤病诊断

Incorporating the Knowledge of Dermatologists to Convolutional Neural  Networks for the Diagnosis of Skin Lesions

Our system addresses Part 1, Lesion Segmentation and Part 3, Lesion
Classification of the ISIC 2017 challenge. Both algorithms make use of deep
convolutional networks to achieve the challenge objective.

本研究利用深度卷积神经网络实现 ISIC 2017 挑战赛的病变分割和分类任务。