In this paper we present the methods of our submission to the ISIC 2018
challenge for skin lesion diagnosis (Task 3). The dataset consists of 10000
images with seven image-level classes to be distinguished by an automated
algorithm. We employ an ensemble of convolutional neural networks for this
task. In particular, we fine-tune pretrained state-of-the-art deep learning
models such as Densenet, SENet and ResNeXt. We identify heavy class imbalance
as a key problem for this challenge and consider multiple balancing approaches
such as loss weighting and balanced batch sampling. Another important feature
of our pipeline is the use of a vast amount of unscaled crops for evaluation.
Last, we consider meta learning approaches for the final predictions. Our team
placed second at the challenge while being the best approach using only
publicly available data.

该研究介绍了应对 ISIC2018 皮肤病变诊断挑战的方法，使用卷积神经网络进行皮肤病变分类，解决了类别不平衡问题，同时采用元学习技术预测结果，并在挑战中取得第二名成绩，是唯一使用公共数据的最佳解决方案。

使用集成、未缩放多重裁剪评估和损失加权的皮肤病变诊断

Skin Lesion Diagnosis using Ensembles, Unscaled Multi-Crop Evaluation  and Loss Weighting

In this paper, a deep neural network based ensemble method is experimented
for automatic identification of skin disease from dermoscopic images. The
developed algorithm is applied on the task3 of the ISIC 2018 challenge dataset
(Skin Lesion Analysis Towards Melanoma Detection).

本研究探讨了一种基于深度神经网络的集成方法，用于从皮肤镜图像中自动识别皮肤疾病，其算法被应用于 ISIC 2018 挑战数据集（皮肤病变分析，以期达到黑色素瘤检测的目的）。