  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.
