As skin cancer is one of the most frequent cancers globally, accurate,
non-invasive dermoscopy-based diagnosis becomes essential and promising. A task
of the Part 3 of the ISIC Skin Image Analysis Challenge at MICCAI 2018 is to
predict seven disease classes with skin lesion images, including melanoma
(MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis /
Bowen's disease (intraepithelial carcinoma) (AKIEC), benign keratosis (solar
lentigo / seborrheic keratosis / lichen planus-like keratosis) (BKL),
dermatofibroma (DF) and vascular lesion (VASC) as defined by the International
Dermatology Society.
In this work, we design the WonDerM pipeline, that resamples the preprocessed
skin lesion images, builds neural network architecture fine-tuned with
segmentation task data (the Part 1), and uses an ensemble method to classify
the seven skin diseases. Our model achieved an accuracy of 0.899 and 0.785 in
the validation set and test set, respectively.

该研究旨在通过深度学习和神经网络应用于皮肤科图像分析挑战解决皮肤病的预测和分类问题，并在验证数据集和测试数据集中分别实现了 0.899 和 0.785 的准确率。