On the official test set our method is ranked first for both tasks with a balanced accuracy of 63. 6% for task 1 and 63. 4% for task 2.
The first problem is the effective use of high-resolution images with pretrained standard architectures for image classification.
In the first stage, we leverage the inter-class variation of the data distribution for the task of conditional image synthesis by learning the inter-class mapping and synthesizing under-represented class samples from the over-represented ones using unpaired image-to-image translation.
As skin cancer is one of the most frequent cancers globally, accurate, non-invasive dermoscopy-based diagnosis becomes essential and promising.