We describe our methods that achieved the 3rd and 4th places in tasks 1 and
2, respectively, at ISIC challenge 2019. The goal of this challenge is to
provide the diagnostic for skin cancer using images and meta-data. There are
nine classes in the dataset, nonetheless, one of them is an outlier and is not
present on it. To tackle the challenge, we apply an ensemble of classifiers,
which has 13 convolutional neural networks (CNN), we develop two approaches to
handle the outlier class and we propose a straightforward method to use the
meta-data along with the images. Throughout this report, we detail each
methodology and parameters to make it easy to replicate our work. The results
obtained are in accordance with the previous challenges and the approaches to
detect the outlier class and to address the meta-data seem to be work properly.

本文介绍了在 ISIC 2019 挑战中获得任务 1 和 2 的第三名和第四名的方法。研究目标是提供使用图像和元数据进行皮肤癌诊断，使用 CNN 集成了分类器，开发了两种处理异常类的方法，并提出了一种简单的方法来使用元数据和图像。

基于深度学习和熵的皮肤癌检测来检测异常样本

Skin cancer detection based on deep learning and entropy to detect  outlier samples

This article summarizes the BCN20000 dataset, composed of 19424 dermoscopic
images of skin lesions captured from 2010 to 2016 in the facilities of the
Hospital Cl\'inic in Barcelona. With this dataset, we aim to study the problem
of unconstrained classification of dermoscopic images of skin cancer, including
lesions found in hard-to-diagnose locations (nails and mucosa), large lesions
which do not fit in the aperture of the dermoscopy device, and hypo-pigmented
lesions. The BCN20000 will be provided to the participants of the ISIC
Challenge 2019, where they will be asked to train algorithms to classify
dermoscopic images of skin cancer automatically.

本篇论文总结了 BCN20000 数据集，该集包含来自巴塞罗那医学中心在 2010 年至 2016 年期间拍摄的 19424 张皮肤镜检影像，旨在研究皮肤癌的非受限分类问题，包括在难以诊断的部位（指甲和黏膜），不适合镜检设备孔径的大型病变和低色素病变。BCN20000 将提供给 ISIC Challenge 2019 的参与者使用，参与者需要训练算法自动分类皮肤癌镜检影像。